Abstract

Huge amounts of data are circulating in the digital world in the era of the Industry 5.0 revolution. Machine learning is experiencing success in several sectors such as intelligent control, decision making, speech recognition, natural language processing, computer graphics, and computer vision, despite the requirement to analyze and interpret data. Due to their amazing performance, Deep Learning and Machine Learning Techniques have recently become extensively recognized and implemented by a variety of real-time engineering applications. Knowledge of machine learning is essential for designing automated and intelligent applications that can handle data in fields such as health, cyber-security, and intelligent transportation systems. There are a range of strategies in the field of machine learning, including reinforcement learning, semi-supervised, unsupervised, and supervised algorithms. This study provides a complete study of managing real-time engineering applications using machine learning, which will improve an application’s capabilities and intelligence. This work adds to the understanding of the applicability of various machine learning approaches in real-world applications such as cyber security, healthcare, and intelligent transportation systems. This study highlights the research objectives and obstacles that Machine Learning approaches encounter while managing real-world applications. This study will act as a reference point for both industry professionals and academics, and from a technical standpoint, it will serve as a benchmark for decision-makers on a range of application domains and real-world scenarios.

1. Introduction

1.1. Machine Learning Evolution

In this digital era, the data source is becoming part of many things around us, and digital recording [1, 2] is a normal routine that is creating bulk amounts of data from real-time engineering applications. This data can be unstructured, semi-structured, and structured. In a variety of domains, intelligent applications can be built using the insights extracted from this data. For example, as in [3] author used cyber-security data for extracting insights and use those insights for building intelligent application for cyber-security which is automated and driven by data. In the article [1], the author uses mobile data for extracting insights and uses those insights for building an intelligent smart application which is aware of context. Real-time engineering applications are based on tools and techniques for managing the data and having the capability for useful knowledge or insight extraction in an intelligent and timely fashion.

Machine Learning is a stream in Artificial Intelligence, which is gaining popularity in recent times in the field of computing and data analysis that will make applications behave intelligently [4]. In industry 4.0 (fourth industrial revolution) machine learning is considered one of the popular technologies which will allow the application to learn from experience, instead of programming specifically for the enhancement of the system [1, 3]. Traditional practices of industries and manufacturing are automated in Industry 4.0 [5] by using machine learning which is considered a smart technology and is used for exploratory data processing. So, machine learning algorithms are keys to developing intelligent real-time engineering applications for real-world problems by analyzing the data intelligently. All the machine learning techniques are categorized into the following types (a) Reinforcement Learning (b) Unsupervised Learning (c) Semi-Supervised Learning, and (d) Supervised Learning.

Based on the collected data from google trends [6], popularity of these techniques is represented in Figure 1. In Figure 1 the y-axis indicated the popularity score of the corresponding technique and the x-axis indicated the time period. As per Figure 1, the popularity score of the technique is growing day by day in recent times. Thus, it gives the motivation to perform this review on machine learning’s role in managing Real Time Engineering Applications. We may use Google Trends to find out what the most popular web subjects are at any given time and location. This could help us generate material and give us suggestions for articles that will most likely appeal to readers. Just make sure the content is relevant to our company or industry. We can look into the findings a little more carefully and investigate the reasons that may have influenced such trends because Google Trends can supply us with data about the specific regions in which our keywords drew substantial interest. With this level of data, we can figure out what’s working and what needs to be improved.

Figure 1 

World wide trend analysis on machine learning techniques [6].

Machine learning algorithms’ performance and characteristics and nature of the data will decide the efficiency and effectiveness of the solution based on machine learning. The data-driven systems [7, 8] can be effectively built by using the following ML areas like reinforcement learning, association rule learning, reduction of dimensionality and feature engineering, data clustering, regression, and classification analysis. From ANN, a new technology is originated from the family of machine learning techniques called Deep Learning which is used for analyzing data intelligently [9]. Every machine learning algorithm’s purpose is different even various machine learning algorithms applied over the same category will generate different outcomes and depends on the nature and characteristics of data [10]. Hence, it’s challenging to select a learning algorithm for generating solutions to a target domain. Thus, there is a need for understanding the applicability and basic principle of ML algorithms in various Real Time Engineering Applications.

A comprehensive study on a variety of machine learning techniques is provided in this article based on the potentiality and importance of ML that can be used for the augmentation of application capability and intelligence. For industry people and academia this article will be acting as a reference manual, to research and study and build intelligent systems which are data-driven in a variety of real-time engineering applications on the basis of machine learning approaches.

1.2. Types of Machine Learning Techniques

Figure 2 shows the Machine Learning Timeline chart. There are 4 classes of machine learning approaches (a) Reinforcement Learning, (b) Semi-Supervised Learning (c) Unsupervised, and (d) Supervised Learning as shown in Figure 3. With the applicability of every ML technique in Real Time Engineering applications, we put down a brief discussion on all the four types of ML approaches as follows:(i)Reinforcement learning: in an environment-driven approach, RL allows machines and software agents to assess the optimal behavior automatically to enhance the efficiency in a particular context [11]. Penalty or rewards are the basis for RL, and the goal of this approach is to perform actions that minimize the penalty and maximize the reward by using the extracted insights from the environment [12]. RL can be used for enhancing sophisticated systems efficiency by doing operational optimization or by using automation with the help of the trained Artificial Intelligence models like supply chain logistics, manufacturing, driving autonomous tasks, robotics, etc.(ii)Semi-supervised: as this method operates on both unlabeled and labeled data [3, 7] it is considered a hybrid approach and lies between “with supervision” and “without supervision” learning approach. The author in [12] concludes that the semi-supervised approach is useful in real-time because of numerous amounts of unlabeled data and rare amounts of labeled data available in various contexts. The semi-supervised approach achieves the goal of predicting better when compared to predictions based on labeled data only. Text classification, labeling data, fraud detection, machine translation, etc., are some of the common tasks.(iii)Unsupervised: as in [7], the author defines that unsupervised approach as a process of data-driven, with minimum or no human interface, it takes datasets consisting of unlabeled data and analyzes them. The unsupervised approach is widely used for purpose of exploring data, results grouping, identifying meaningful structures and trends, and extracting general features. Detecting anomalies, association rules finding, reducing dimensionality, learning features, estimating density, and clustering are the most usual unsupervised tasks.(iv)Supervised: as in [7], author defines the supervised approach as a process of making a function to learn to map output from input. A function is inferred by using training example collection and training data which is labeled. As in [3], the author states that a supervised learning approach is a task-driven approach, which is to be initiated when certain inputs are capable to accomplish a variety of goals. The most frequently used supervised learning tasks are regression and classification.

Figure 2 

Machine learning time line.

Figure 3 

Machine learning techniques.

In Table 1, we summarize various types of machine learning techniques with examples.

Table 1 

ML Technique varieties with approaches and examples.

Table 2 summarizes the comparison between the current survey with existing surveys and highlights how it is different or enhanced from the existing surveys.

Table 2 

Summary of important surveys on ML.

1.3. Contributions

Following are the key contributions to this article:(i)A comprehensive view on variety of ML algorithms is provided which is applicable to improve data-driven applications, task-driven applications capabilities, and intelligence(ii)To discuss and review the applicability of various solutions based on ML to a variety of real-time engineering applications(iii)By considering the data-driven application capabilities and characteristics and nature of the data, the proposed study/review scope is defined(iv)Various challenges and research directions are summarized and highlighted that fall within this current study scope

1.4. Paper Organization

The organization of the rest of the article is as follows: state of art is presented in the next section which explains and introduces real-time engineering applications and machine learning; in the next section, ML’s role in real-time engineering applications is discussed; and in the coming section, challenges and lessons learned are presented; in the penultimate section, several future directions and potential research issues are discussed and highlighted; and in the final section conclude the comprehensive study on managing Real Time Engineering Applications using Machine Learning.

2. State of the Art

2.1. Real World Issues

Computer systems can utilize all client data through machine learning. It acts according to the program’s instructions while also adapting to new situations or changes. Algorithms adapt to data and exhibit previously unprogrammed behaviors. Acquiring the ability to read and recognize context enables a digital assistant to skim emails and extract vital information. This type of learning entails the capacity to forecast future customer behaviors. This enables you to have a deeper understanding of your customers and to be proactive rather than reactive. Machine learning is applicable to a wide variety of sectors and industries and has the potential to expand throughout time. Figure 4 represents the real-world applications of machine learning.

Figure 4 

Applications of machine learning.

2.2. Introduction to Cyber Security

For both, services and information, internet is most extensively exploited. In article [13], author summarizes that since 2017 as an information source Internet is utilized by almost 48% of the whole population in the world. As concluded in the article [14], this number is hiked up to 82% in advanced countries.

The interconnection of distinct devices, networks, and computers is called the Internet, whose preliminary job is to transmit information from one device to another through a network. Internet usage spiked due to the innovations and advancements in mobile device networks and computer systems. As internet is the mostly used by the majority of population as an information source so it’s more prone to cyber criminals [15]. A computer system is said to be stable when it’s offering integrity, availability, and confidentiality of information. As stated in the article [16], with intent to disturb normal activity, if an unauthorized individual enters into the network, then the computer system will be compromised with integrity and security. User assets and cyberspace can be secured from unauthorized individual attacks and access with the help of cyber security. As in article [17], the primary goal of cyber security is to keep information available, integral, and confidential.

2.3. Introduction to Healthcare

With advancements in the field of Deep Learning/Machine Learning, there are a lot of transformations happening in the areas like governance, transportation, and manufacturing. Extensive research is going on in the field of Deep Learning over the last decade. Deep Learning has been applied to lots of areas that delivered a state-of-the-art performance in variety of domains like speech processing, text analytics, and computer vision. Recently researchers started deploying Deep Learning/Machine Learning approaches to healthcare [18], and they delivered outstanding performances in the jobs like brain tumor segmentation [19], image reconstruction in medical images [20, 21] lung nodule detection [22], lung disease classification [23], identification of body parts [24], etc.

It is evident that CAD systems that provide a second opinion will help the radiologists to confirm the disease [25] and deep learning/machine learning will further enhance the performance of these CAD systems and other systems that will provide supporting decisions to the radiologists [26].

Advancement in the technologies like big data, mobile communication, edge computing, and cloud computing is also helping the deployment of deep learning/machine learning models in the domain of healthcare applications [27]. By combining they can achieve greater predictive accuracies and an intelligent solution can be facilitated which is human-centered [28].

2.4. Introduction to Intelligent Transportation Systems

In transit and transportation systems, after the deployment of sensing technologies, communication, and information, the resultant implementation is called an intelligent transportation system [29]. An intelligent transportation system is an intrinsic part of smart cities [30], which have the following services such as autonomous vehicles, public transit system management, traveler information systems, and road traffic management. These services are expected to contribute a lot to the society by curbing pollution, enhancing energy efficiency, transit and transportation efficiency is enhanced and finally, traffic and road safety is also improved.

Advances in technologies like wireless communication technology, computing, and sensing are enabling intelligent transportation systems applications and also bear a lot of challenges due to their capabilities to generate huge amounts of data, independent QoS requirements, and scalability.

Due to the recent traction in deep learning/machine learning models, approaches like RL and DL are utilized to exploit patterns and generate decisions and predictions accurately [31–33].

2.5. Introduction to Renewable Energy

Sustainable and alternative energy sources are in demand due to the effect created by burning fossil fuels in the environment and fossil fuel depletion. As in article [34], the energy market biomass, wind power, tidal waves, geothermal, solar thermal, and solar photovoltaic are growing as renewable energy resources. There will be instability in the power grids due to various reasons like when demand is more than the supply of the energy and when supply is more than the demand of the energy. Finally, environmental factors affect the energy output of the plants based on the renewable energy. To address the management and optimization of energy, machine learning is used.

2.6. Introduction to Smart Manufacturing

Manufacturing has been divided into a number of categories, one of the categories in which computer-based manufacturing is performed is called Smart Manufacturing, which performs workers’ training, digital technology, and quick changes in the design and with high adaptability. Other responsibilities include recyclability of production effectively, supply chain optimization, and demand-based quick changes in the levels of production. Enabling technologies of Smart Manufacturing are advances in robotics, services and devices connectivity in the industry, and processing capabilities in the big data.

2.7. Introduction to Smart Grid

The basic structure of the electrical power grid has remained same over time, and it has been noticed that it has become outdated and ill-suited, unable to meet demand and supply in the twenty-first century. Even though we are in the twenty-first century, electrical infrastructure has remained mostly unaltered throughout time. However, as the population and consumption have grown, so requires power.

2.7.1. Drawbacks

(i)Analyzing the demand is difficult(ii)Response time is slow

The new smart grid idea has evolved to address the issues of the old outdated electrical power system. SG is a large energy network that employs real time and intelligent monitoring, communication, control, and self-healing technologies to provide customers with a variety of alternatives while guaranteeing the stability and security of their electricity supply. By definition, SGs are sophisticated cyber-physical system. The functionality of this modern SG can be broken down into four parts.

This contemporary SG’s functionality may be split down into four components:(1)Consumption: electricity is used for a variety of reasons by various industries and inhabitants(2)Distribution: the power so that it may be distributed more widely(3)Transmission: electricity is transmitted over a high-voltage electronic infrastructure(4)Generation: during this phase, electricity is generated in a variety of methods

ML and DL functionalities in the context of SG include predicting about(1)Stability of the SG(2)Optimum schedule(3)Fraud detection(4)Security breach detection(5)Network anomaly detection(6)Sizing(7)Fault detection(8)Energy consumption(9)Price(10)Energy generation

2.8. Introduction to Computer Networks

The usefulness of ML in networking is aided by key technological advancements in networking, such as network programmability via Software-Defined Networking (SDN). Though machine learning has been widely used to solve problems such as pattern recognition, speech synthesis, and outlier identification, its use in network operations and administration has been limited. The biggest roadblocks are determining what data may be collected and what control actions can be taken on legacy network equipment. These issues are alleviated by the ability to program the network using SDN. ML-based cognition can be utilized to help automate network operation and administration chores. As a result, applying machine learning approaches to such broad and complicated networking challenges is both intriguing and challenging. As a result, ML in networking is a fascinating study area that necessitates a thorough understanding of ML techniques as well as networking issues.

2.9. Introduction to Energy Systems

A set of structured elements designed for the creation, control, and/or transformation of energy is known as an energy system [35, 36]. Mechanical, chemical, thermal, and electro-magnetical components may be combined in energy systems to span a wide variety of energy categories, including renewables and alternative energy sources [37–39]. The progress of energy systems faces difficult decision-making duties in order to meet a variety of demanding and conflicting objectives, such as functional performance, efficiency, financial burden, environmental effect, and so on [40]. The increasing use of data collectors in energy systems has resulted in an enormous quantity of data being collected. Smart sensors are increasingly widely employed in the production and consumption of energy [41–43]. Big data has produced a plethora of opportunities and problems for making well-informed decisions [44, 45]. The use of machine learning models has aided the deployment of big data technologies in a variety of applications [46–50]. Prediction approaches based on machine learning models have gained popularity in the energy sector [51–53] because they make it easier to infer functional relationships from observations. Because of their accuracy, effectiveness, and speed, ML models in energy systems are becoming crucial for predictive modeling of production, consumption, and demand analysis [54, 55]. In the context of complex human interactions, ML models provide give insight into energy system functioning [56, 57]. The use of machine learning models is in making traditional energy systems, as well as alternative and renewable energy systems.

3. Recent Works on Real-Time Engineering Applications

3.1. Machine Learning for ITS

Exposure to traffic noise, air pollution, road injuries, and traffic delays are only some of the key issues that urban inhabitants experience on a daily basis. Urban areas are experiencing severe environmental and quality-of-life difficulties as a result of rapid car expansion, insufficient transportation infrastructure, and a lack of road safety rules. For example, in many urban areas, large trucks violate the typical highways, resulting in traffic congestion and delays. In addition, many bikers have frequent near misses as a result of their clothes, posture changes, partial occlusions, and varying observation angles all posing significant challenges to the Machine Learning (ML) algorithms’ detection rates.

Over the last decade, there has been a surge in interest in using machine learning and deep learning methods to analyze and visualize massive amounts of data generated from various sources in order to improve the classification and recognition of pedestrians, bicycles, special vehicles (e.g., emergency vehicles vs. heavy trucks), and License Plate Recognition (LPR) for a safer and more sustainable environment. Although deep models are capable of capturing a wide variety of appearances, adaption to the environment is essential.

Artificial neural networks form the base for deep learning success; in artificial neural networks to mirror an image, the human brain functioning interconnected node system sets are present. The neighboring layer’s nodes will be consisting of connections with weights coming from nodes from other layers. The output value is generated by given input and weight to the activation function in a node. Figure 5 presents the ML mainstream approaches used in ITS.

Figure 5 

Mainstream ML approaches.

Figure 6 shows the RL working in intelligent transportation system.

Figure 6 

RL working in intelligent transportation system.

Figures 7–9 present the interaction between ITS and ML and Machine Learning Pipeline.

Figure 7 

ML pipeline and interaction between ITS and ML.

Figure 8 

ML pipeline.

Figure 9 

Interaction between ML and ITS.

3.2. Machine Learning for HealthCare

Over time, for the actions performed as a response reward, actions and observations are given as input to policy functions, and the method that learns from this policy function is called RL [58]. There is a wide range of healthcare applications where RL can be used even RL can be used in the detection of disease based on checking symptoms ubiquitously [59]. Another potential use of RL in this domain is Gogame [60].

In semi-supervised learning, both unlabeled data and labeled data are used for training particularly greater doses of unlabeled data and little doses of labeled data are available, and then semi-supervised learning is suitable. Semi-supervised learning can be applied to a variety of healthcare applications like medical image segmentation [61, 62] using various sensors recognition of activity is proposed in [61], in [63] author used semi-supervised learning for healthcare data clustering.

In supervised learning, labeled information is used for training the model to map the input to output. In the regression output value is continuous and in classification output value is discrete. Typical application of supervised learning in the healthcare domain is the identification of organs in the body using various image modalities [19] and nodule classification in the lung images [21].

In unsupervised learning, mapping of input to the output will be done by training the model using unlabeled data:(i)Similarity is used for clustering(ii)Feature selection/dimensionality reduction(iii)Anomaly detection [64]

Unsupervised learning can be applied to a lot of healthcare applications like feature selection [65] using PCA and using Clustering [66] for heart disease prediction.

Various phases in an ML-based Healthcare system are shown in Figure 10.

Figure 10 

ML-based healthcare systems phases of development.

The four major applications of healthcare that can benefit from ML/DL techniques are prognosis, diagnosis, treatment, and clinical workflow, which are described in Table

Neural networks comparison.

3.3. Machine Learning for Cyber Security

Artificial Intelligence and Machine Learning are widely accepted and utilized in various fields like Cyber Security [94–103], design and manufacturing [104], medicine [105–108], education [109], and finance [110–112]. Machine Learning techniques are used widely in the following areas of cyber security intrusion detection [113–116], dark web or deep web sites [117, 118], phishing [119–121], malware detection [122–125], fraud detection , and spam classification . As time changes there is a need for vigorous and novel techniques to address the issues of cyber security. Machine Learning is suitable for evolutionary attacks as it learns from experiences.

In article the authors analyzed and evaluated the dark web which is a hacker’s social network by using the ML approach for threat prediction in the cyberspace. In article , the author used an ML model with social network features for predicting cyberattacks on an organization during a stipulated period. This prediction uses a dataset consisting of darkweb’s 53 forum’s data in it. Advancements in recent areas can be found in .

Antivirus, firewalls, unified threat management , intrusion prevention system , and SEIM solutions are some of the classical cyber security systems. As in article , the author concluded that, in terms of post-cyber-attack response, performance, and in error rate classical cyber security systems are poor when compared with AI-based systems. As in the article , once there is cyberspace damage by the attack then only it’s identified and this situation happens in almost 60%. Both on the cyber security side and attackers’ side, there is a stronger hold by ML. On the cyber security side as specified in this article to safeguard everything from the damage done by the attackers and for detecting attacks at an early stage and finally for performance enhancement ML is used. ML is used on the attacker’s end to locate weaknesses and system vulnerabilities as well as techniques to get beyond firewalls and other defence walls As in , the author concludes that to further enhance the classification performance ML approaches are combined.

3.4. Machine Learning for Renewable Energy

Forecasting Renewable Energy Generation can be done using Machine Learning, state-of-art works are presented in Table 4.

3.5. Machine Learning for Smart Manufacturing

The following table shows the ML applicability to Smart Manufacturing. State-of-art works are presented in Table 5.

ML state-of-the-art systems in the smart manufacturing domain.

3.6. Machine Learning for Smart Grids

This subsection discusses machine learning applicability to smart grids. State-of-the-art works are presented in Table 6.

ML state-of-the-art systems in smart grids domain.

3.7. Machine Learning for Computer Networks

3.7.1. Traffic Prediction

As networks are day by day becoming diverse and complex, it becomes difficult to manage and perform network operations so huge importance is given to traffic forecast in the network to properly manage and perform network operations. Time Series Forecasting is forecasting the traffic in near future.

3.7.2. Traffic Prediction

To manage and perform network operations, it’s quite important to perform classification of the network traffic which includes provisioning of the resource, monitoring of the performance, differentiation of the service and quality of service, intrusion detection and security, and finally capacity planning.

3.7.3. Congestion Control

In a network, excess packets will be throttled using the concept called congestion control. It makes sure the packet loss ratio is in an acceptable range, utilization of resources is at a fair level, and stability of the network is managed.

Table 7 presents ML state of art systems in networking.

ML state-of-the-art systems in computer networking domain.

3.7.4. Machine Learning for Civil Engineering

The first uses of ML programs in Civil Engineering involved testing different existing tools on simple programs [210–213], more difficult problems are addressed in .

3.7.5. Machine Learning for Energy Systems

Hybrid ML models, ensembles, Deep Learning, Decision Trees, ANFIS, WNN, SVM, ELM, MLP, ANN are among the ten key ML models often employed in energy systems, according to the approach.

Table 8 presents ML state of art systems in the Energy Systems domain.

ML state-of-the-art systems in energy systems domain.

4. Current Challenges on Machine Learning Technology

While machine learning offers promise and is already proving beneficial to businesses around the world, it is not without its hurdles and issues. For instance, machine learning is useful for spotting patterns, but it performs poorly at generalizing knowledge. There is also the issue of “algorithm weariness” among users.

In ML, for model training, decent amount of data and resources that provide high performances are needed. This challenge is addressed by involving multiple GPU’s. In Real Time Engineering Applications, an ML approach is needed which is modeled to address a particular problem robustly. As the same model designed to address one task in real-time engineering application cannot address all the tasks in a variety of domains, so there is a need to design a model for each task in the Real Time Engineering Applications.

ML approaches should have the skill to prevent issues in the early stages as this is an important challenge to address in most real-time engineering applications. In the medical domain, ML can be used in predicting diseases and ML techniques can also be used for forecasting the detection of terrorism attacks. As in , the catastrophic consequences cannot be avoided by having faith blindly in the ML predictions. As in article , author states that ML approaches are used in various domains, but in some domains as an alternative to accuracy and speed ML approaches require correctness at very high levels. To convert a model into trustworthy, there is a need to avoid a shift in dataset, which means the model is to be trained and tested on the same dataset which can be ensured by avoiding data leakages .

Moving object’s location can be identified by using the enabling technologies like GPS and cell phones and this information to be maintained securely as tamper-proof is one of the crucial tasks for ML. As in article , author states that an object’s location information from multiple sources is compared and tries to find the similarity, and as in article author confirms that due to network delays the location change of the objects there is always ambiguousness in the location information gathered from multiple sources and the trustworthiness of such information needs to be addressed using ML techniques.

In a connected web system, to have interaction between consumers and service providers with trustworthiness an ontology of trust is proposed in the article . In text classification also trustworthiness is used. As in article author states that in semantic and practical terms where the meaning of the text is interpreted trustworthiness can be fused. In article author validates the software’s trustworthiness using a metric model. As in article , the author states that in companies and data centers the consumption of power can be mitigated by utilizing ML approaches for designing strategies that are power-aware. To reduce the consumption in its entirety, it’s better to turn off the machines dynamically. Which machine to be turned off will be decided by the forecasting model and it’s very important to have trust in this forecasting model before setting up the machine to be switched off.

Fatigue in the alarm is generating false alarms at higher rates. This will reduce the response time of the security staff and this issue is an interesting area in cyber security .

Some concerns associated with machine learning have substantial repercussions that are already manifesting now. One is the absence of explainability and interpretability, also known as the “black box problem.” Even its creators are unable to comprehend how machine learning models generate their own judgments and behaviors. This makes it difficult to correct faults and ensure that a model’s output is accurate and impartial. When it was discovered that Apple’s credit card algorithm offered women much lesser credit lines than men, for instance, the corporation was unable to explain the reason or address the problem.

This pertains to the most serious problem affecting the field: data and algorithmic bias. Since the debut of the technology, machine learning models have been frequently and largely constructed using data that was obtained and labeled in a biassed manner, sometimes on intentionally. It has been discovered that algorithms are frequently biased towards women, African Americans, and individuals of other ethnicities. Google’s DeepMind, one of the world’s leading AI labs, issued a warning that the technology poses a threat to queer individuals.

This issue is pervasive and well-known, yet there is resistance to taking the substantial action that many experts in the field insist is necessary. Timnit Gebru and Margaret Mitchell, co-leaders of Google’s ethical AI team, were sacked in retaliation for Gebru’s refusal to retract research on the dangers of deploying huge language models, according to tens of thousands of Google employees. In a survey of researchers, policymakers, and activists, the majority expressed concern that the progress of AI by 2030 will continue to prioritize profit maximization and societal control over ethics. The nation as a whole is currently debating and enacting AI-related legislation, particularly with relation to immediately and blatantly damaging applications, like facial recognition for law enforcement. These discussions will probably continue. And the evolving data privacy rules will soon influence data collecting and, by extension, machine learning.

5. Machine Learning Applications

Because of its ability to make intelligent decisions and its potential to learn from the past, machine learning techniques are more popular in industry 4.0.

Here we discuss and summarize various machine learning techniques application areas.

5.1. Intelligent Decision-Making and Predictive Analytics

By making use of data-driven predictive analytics, intelligent decisions are made by applying machine learning techniques . To predict the unknown outcomes by relying on the earlier events by exploiting and capturing the relationship between the predicted variables and explanatory variables is the basis for predictive analytics , for example, credit card fraud identification and criminal identification after a crime. In the retail industry, predictive analytics and intelligent decision-making can be used for out-of-stock situation avoidance, inventory management, behavior, and preferences of the consumer are better understood and logistics and warehouse are optimized. Support Vector Machines, Decision Trees, and ANN are the most widely used techniques in the above areas . Predicting the outcome accurately can help every organization like social networking, transportation, sales and marketing, healthcare, financial services, banking services, telecommunication, e-commerce, industries, etc., to improve.

5.2. Cyber-Security and Threat Intelligence

Protecting data, hardware, systems, and networks is the responsibility of cyber-security and this is an important area in Industry 4.0 . In cyber-security, one of the crucial technologies is machine learning which provides protection by securing cloud data, while browsing keeps people safe, foreseen the bad people online, insider threats are identified and malware is detected in the traffic. Machine learning classification models , deep learning-based security models , and association rule learning techniques are used in cyber-security and threat intelligence.

5.3. Smart Cities

In IoT, all objects are converted into things by equipping objects with transmitting capabilities for transferring the information and performing jobs with no user intervention.

Some of the applications of IoT are business, healthcare, agriculture, retail, transportation, communication, education, smart home, smart governance , and smart cities . Machine learning has become a crucial technology in the internet of things because of its ability to analyze the data and predict future events . For instance, congestion can be predicted in smart cities, take decisions based on the surroundings knowledge, energy estimation for a particular period, and predicting parking availability.

5.4. Transportation and Traffic Prediction

Generally, transportation networks have been an important part of every country’s economy. Yet, numerous cities across the world are witnessing an enormous amount of traffic volume, leading to severe difficulties such as a decrease in the quality of life in modern society, crises, accidents, CO₂ pollution increased, higher fuel prices, traffic congestion, and delays . As a result, an ITS, that predicts traffic and is critical, and it is an essential component of the smart city. Absolute forecasting of traffic based on deep learning and machine learning models can assist to mitigate problems . For instance, machine learning may aid transportation firms in identifying potential difficulties that may arise on certain routes and advising that their clients choose an alternative way based on their history of travel and pattern of travel by taking variety of routes. Finally, by predicting and visualizing future changes, these solutions will assist to optimize flow of the traffic, enhance the use and effectiveness of sustainable forms of transportation, and reduce real-world disturbance.

5.5. Healthcare and COVID-19 Pandemic

In a variety of medical-related application areas, like prediction of illness, extraction of medical information, data regularity identification, management of patient data, and so on, machine learning may assist address diagnostic and prognostic issues . Here in this article , coronavirus is considered as an infectious disease by the WHO. Learning approaches have recently been prominent in the fight against COVID-19 .

Learning approaches are being utilized to categorize the death rate, patients at high risk, and various abnormalities in the COVID-19 pandemic . It may be utilized to fully comprehend the virus’s origins, predict the outbreak of COVID-19, and diagnose and treat the disease . Researchers may use machine learning to predict where and when COVID-19 will spread, and then inform those locations to make the necessary preparations. For COVID 19 pandemic , to address the medical image processing problems, deep learning can provide better solutions. Altogether, deep and machine learning approaches can aid in the battle against the COVID-19 virus and pandemic, and perhaps even the development of intelligent clinical judgments in the healthcare arena.

5.6. Product Recommendations in E-commerce

One of the most prominent areas in e-commerce where machine learning techniques are used is suggesting products to the users of the e-commerce. Technology of machine learning can help e-commerce websites to analyze their customers’ purchase histories and provide personalized product recommendations based on their behavior and preferences for their next purchase. By monitoring browsing tendencies and click-through rates of certain goods, e-commerce businesses, for example, may simply place product suggestions and offers. Most merchants, such as flipkart and amazon, can avert out-of-stock problems, manage better inventory, optimize storage, and optimize transportation by using machine learning-based predictive models. Future of marketing and sales is to improve the personalized experience of the users while purchasing the products by collecting their data and analyzing the data and use them to improve the experience of the users. In addition, to attract new customers and also to retain the existing ones the e-commerce website will build packages to attract the customers and keep the existing ones.

5.7. Sentiment Analysis and NLP (Natural Language Processing)

An act of using a computer system to read and comprehend spoken or written language is called Natural Language Processing. Thus, NLP aids computers in reading texts, hearing speech, interpreting it, analyzing sentiment, and determining which elements are important, all of which may be done using machine learning techniques. Some of the examples of NLP are machine translation, language translation, document description, speech recognition, chatbot, and virtual personal assistant. Collecting data and generating views and mood of the public from news, forums, social media, reviews, and blogs is the responsibility of sentiment analysis which is a sub-field of NLP. In sentiment analysis, texts are analyzed by using machine learning tasks to identify the polarity like neutral, negative and positive and emotions like not interested, have interest, angry, very sad, sad, happy, and very happy.

5.8. Image, Speech and Pattern Recognition

Machine Learning is widely used in the image recognition whose task is to detect the object in an image. Some of the instances of image recognition are social media suggestions tagging, face detection, character recognition and cancer label on an X-ray image. Alexa, Siri, Cortana, Google Assistant etc., are the famous linguistic and sound models in speech recognition [286282]. The automatic detection of patterns and data regularities, such as picture analysis, is characterized as pattern recognition . Several machine learning approaches are employed in this field, including classification, feature selection, clustering, and sequence labeling.

5.9. Sustainable Agriculture

Agriculture is necessary for all human activities to survive . Sustainable agriculture techniques increase agricultural output while decreasing negative environmental consequences. In article authors convey those emerging technologies like mobile devices, mobile technologies, Internet of Things can be used to capture the huge amounts of data to encourage the adoption of practices of sustainable agriculture by encouraging knowledge transfer among farmers. By using technologies, skills, information knowledge-intensive supply chains are developed in sustainable agriculture. Various techniques of machine learning can be applied in processing phase of the agriculture, production phase and preproduction phase, distribution phases like consumer analysis, inventory management, production planning, demand estimation of livestock, soil nutrient management, weed detection, disease detection, weather prediction, irrigation requirements, soil properties, and crop yield prediction.

5.10. Context-Aware and Analytics of User Behavior

Capturing information or knowledge about the surrounding is called context-awareness and tunes the behaviors of the system accordingly . Hardware and software are used in context-aware computing for automating the collection and interpreting of the data.

From the historical data machine learning will derive knowledge by using their learning capabilities which is used for bringing tremendous changes in the mobile app development environment.

Smart apps can be developed by the programmers, using which uses can be entertained, support is provided for the user and human behavior is understood and can build a variety of context-aware systems based on data-driven approaches like context-aware smart searching, smart interruption management, smart mobile recommendation, etc., for instance, as in phone call app can be created by using association rules with context awareness. Clustering approaches are used and classification methods are used for predicting future events and for capturing users’ behavior.

6. Challenges and Future Research Directions

In this review, quite a few research issues are raised by studying the applicability of variety of ML approaches in the analysis of applications and intelligent data. Here, opportunities in research and potential future directions are summarized and discussed.

Research directions are summarized as follows:(i)While dealing with real-world data, there is a need for focusing on the in-detail study of the capturing techniques of data(ii)There is a huge requirement for fine-tuning the preprocessing techniques or to have novel data preprocessing techniques to deal with real-world data associated with application domains(iii)Identifying the appropriate machine learning technique for the target application is also one of the research interests(iv)There is a huge interest in the academia in existing machine learning hybrid algorithms enhancement or modification and also in proposing novel hybrid algorithms for their applicability to the target applications domain

Machine learning techniques’ performance over the data and the data’s nature and characteristics will decide the efficiency and effectiveness of the machine learning solutions. Data collection in various application domains like agriculture, healthcare, cyber-security etc., is complicated because of the generation of huge amounts of data in very less time by these application domains. To proceed further in the analysis of the data in machine learning-based applications relevant data collection is the key factor. So, while dealing with real-world data, there is a need for focusing on the more deep investigation of the data collection methods.

There may be many outliers, missing values, and ambiguous values in the data that is already existing which will impact the machine learning algorithms training. Thus, there is a requirement for the cleansing of collected data from variety of sources which is a difficult task. So, there is a need for preprocessing methods to be fine-tuned and novel preprocessing techniques to be proposed that can make machine learning algorithms to be used effectively.

Choosing an appropriate machine learning algorithm best suited for the target application, for the extraction insights, and for analyzing the data is a challenging task, because the characteristics and nature of the data may have an impact on the outcome of the different machine learning techniques [10]. Inappropriate machine learning algorithm will generate unforeseen results which might reduce the accuracy and effectiveness of the model. For this purpose, the focus is on hybrid models, and these models are fine-tuned for the target application domains or novel techniques are to be proposed.

Machine learning algorithms and the nature of the data will decide the ultimate success of the applications and their corresponding machine learning-based solutions. Machine Learning models will generate less accuracy and become useless when the data is the insufficient quantity for training, irrelevant features, poor quality, and non-representative and bad data to learn. For an intelligent application to be built, there are two important factors i.e., various learning techniques handling and effective processing of data.

Our research into machine learning algorithms for intelligent data analysis and applications raises a number of new research questions in the field. As a result, we highlight the issues addressed, as well as prospective research possibilities and future initiatives, in this section.

The nature and qualities of the data, as well as the performance of the learning algorithms, determine the effectiveness and efficiency of a machine learning-based solution. To gather information in a specific domain, such as cyber security, IoT, healthcare, agriculture, and so on. As a result, data for the target machine learning-based applications is collected. When working with real-world data, a thorough analysis of data collection methodologies is required. Furthermore, historical data may contain a large number of unclear values, missing values, outliers, and data that has no meaning.

Many machine learning algorithms exist to analyze data and extract insights; however, the ultimate success of a machine learning-based solution and its accompanying applications is largely dependent on both the data and the learning algorithms. Produce reduced accuracy if the data is bad to learn, such as non-representative, poor-quality, irrelevant features, or insufficient amount for training. As a result, establishing a machine learning-based solution and eventually building intelligent applications, correctly processing the data, and handling the various learning algorithms is critical.

7. Conclusion and Future Scopre

In this study on machine learning algorithms, a comprehensive review is conducted for applications and intelligent analysis of data. Here, the real-world issues and how solutions are prepared by using a variety of learning algorithms are discussed briefly. Machine Learning techniques’ performance and characteristics of the data will decide the machine learning model’s success. To generate intelligent decision-making, machine learning algorithms need to be acquainted with target application knowledge and trained with data collected from various real-world situations. For highlighting the applicability of ML approaches to variety of issues in the real world and variety of application areas are discussed in this review. At last, research directions and other challenges are discussed and summarized. All the challenges in the target applications domain must be addressed by using solutions effectively. For both industry professionals and academia, this study will serve as a reference point and from the technical perspective, this study also works as a benchmark for the decision makers on a variety of application domains and various real-world situations. Machine Learning’s application is not restricted to any one sector. Rather, it is spreading across a wide range of industries, including banking and finance, information technology, media and entertainment, gaming, and the automobile sector. Because the breadth of Machine Learning is so broad, there are several areas where academics are trying to revolutionize the world in the future.

In this Machine learning tutorial, you will learn different types of machine learning including ML algorithms, classification in machine learning, regression in machine learning, clustering in machine learning, dimensionality reduction in machine learning, and their use cases.

There are different ways in which a machine learns. In some cases, we train them and, in some other cases, machines learn on their own. Well, primarily, there are four types of machine learning – Supervised Learning, Unsupervised Learning, Semi-supervised Learning, and Reinforcement. In this module, we are going to discuss the types of machine learning in detail.

In case you want to jump right into a particular topic, here’s the table of contents for this module:

• Types of Machine Learning
• Supervised Machine Learning
  • Categories of Supervised Machine Learning
  • Advantages and Disadvantages of Supervised Learning
  • Applications of Supervised Learning
• Unsupervised Machine Learning
  • Categories of Unsupervised Machine Learning
  • Advantages and Disadvantages of Unsupervised Learning
  • Applications of Unsupervised Learning
• Semi-Supervised Learning
  • Advantages and Disadvantages of Semi-Supervised Learning
• Reinforcement Learning
  • Categories of Reinforcement Learning
  • Applications of Reinforcement Learning
  • Advantages and Disadvantages of Reinforcement Learning
• Conclusion

Types of Machine Learning

Machine Learning is a subset of AI, which enables the machine to automatically learn from data, improve performance from past experiences, and make predictions. Machine learning contains a set of algorithms that work on a huge amount of data. Data is fed to these algorithms to train them, and on the basis of training, they build the model & perform a specific task.

These ML algorithms help to solve different business problems like Regression, Classification, Forecasting, Clustering, Associations, etc.

Based on the methods and ways of learning, machine learning is divided into mainly four types, which are:

1. Supervised Machine Learning
2. Unsupervised Machine Learning
3. Semi-Supervised Machine Learning
4. Reinforcement Learning

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Supervised Machine Learning

As the name suggests, Supervised machine learning is based on supervision. It means in the supervised learning technique, we train the machines using the “labelled” dataset, and based on the training, the machine predicts the output. Here, the labelled data specifies that some of the inputs are already mapped to the output. More preciously, we can say; first, we train the machine with the input and corresponding output, and then we ask the machine to predict the output using the test dataset.

Let’s understand supervised learning with an example. Suppose we have an input dataset of cat and dog images. So, first, we will provide the training to the machine to understand the images, such as the shape & size of the tail of a cat and a dog, the Shape of eyes, color, and height (dogs are taller, cats are smaller), etc. After completion of training, we input the picture of a cat and ask the machine to identify the object and predict the output. Now, the machine is well trained, so it will check all the features of the object, such as height, shape, color, eyes, ears, tail, etc., and find that it’s a cat. So, it will put it in the Cat category. This is the process of how the machine identifies the objects in Supervised Learning.

The main goal of the supervised learning technique is to map the input variable(x) with the output variable(y). Some real-world applications of supervised learning are Risk Assessment, Fraud Detection, Spam filtering, etc.

Categories of Supervised Machine Learning

Supervised machine learning can be classified into two types of problems, which are given below:

• Classification
• Regression

a) Classification

Classification algorithms are used to solve the classification problems in which the output variable is categorical, such as “Yes” or No, Male or Female, Red or Blue, etc. The classification algorithms predict the categories present in the dataset. Some real-world examples of classification algorithms are Spam Detection, Email filtering, etc.

Some popular classification algorithms are given below:

• Random Forest Algorithm
• Decision Tree Algorithm
• Logistic Regression Algorithm
• Support Vector Machine Algorithm

b) Regression

Regression algorithms are used to solve regression problems in which there is a linear relationship between input and output variables. These are used to predict continuous output variables, such as market trends, weather predictions, etc.

Some popular Regression algorithms are given below:

• Simple Linear Regression Algorithm
• Multivariate Regression Algorithm
• Decision Tree Algorithm
• Lasso Regression

Advantages and Disadvantages of Supervised Learning

Advantages:

• Since supervised learning work with the labeled dataset so we can have an exact idea about the classes of objects.
• These algorithms are helpful in predicting the output on the basis of prior experience.

Disadvantages:

• These algorithms are not able to solve complex tasks.
• It may predict the wrong output if the test data is different from the training data.
• It requires lots of computational time to train the algorithm.

Applications of Supervised Learning

Some common applications of Supervised Learning are given below:

• Image Segmentation: Supervised Learning algorithms are used in image segmentation. In this process, image classification is performed on different image data with pre-defined labels.
• Medical Diagnosis: Supervised algorithms are also used in the medical field for diagnosis purposes. It is done by using medical images and past labeled data with labels for disease conditions. With such a process, the machine can identify a disease for the new pat
• Fraud Detection: Supervised Learning classification algorithms are used for identifying fraud transactions, fraud customers, etc. It is done by using historic data to identify the patterns that can lead to possible fraud.
• Spam detection: In spam detection & filtering, classification algorithms are used. These algorithms classify an email as spam or not spam. The spam emails are sent to the spam folder.
• Speech Recognition: Supervised learning algorithms are also used in speech recognition. The algorithm is trained with voice data, and various identifications can be done using the same, such as voice-activated passwords, voice commands, etc.

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Unsupervised Machine Learning

Unsupervised learning is different from the Supervised learning technique; as its name suggests, there is no need for supervision. It means that in unsupervised machine learning, the machine is trained using the unlabeled dataset, and the machine predicts the output without any supervision.

In unsupervised learning, the models are trained with data that is neither classified nor labelled, and the model acts on that data without any supervision.

The main aim of the unsupervised learning algorithm is to group or categorize the unsorted dataset according to the similarities, patterns, and differences. Machines are instructed to find the hidden patterns from the input dataset.

Let’s take an example to understand it more preciously; suppose there is a basket of fruit images, and we input it into the machine learning model. The images are totally unknown to the model, and the task of the machine is to find the patterns and categories of the objects.

So, now the machine will discover its patterns and differences, such as color difference, and shape difference, and predict the output when it is tested with the test dataset.

Categories of Unsupervised Machine Learning

Unsupervised Learning can be further classified into two types, which are given below:

• Clustering
• Association

Clustering

The clustering technique is used when we want to find the inherent groups from the data. It is a way to group the objects into a cluster such that the objects with the most similarities remain in one group and have fewer or no similarities with the objects of other groups. An example of the clustering algorithm is grouping the customers by their purchasing behaviour.

Some of the popular clustering algorithms are given below:

• K-Means Clustering algorithm
• Mean-shift algorithm
• DBSCAN Algorithm
• Principal Component Analysis
• Independent Component Analysis

Association

Association rule learning is an unsupervised learning technique, which finds interesting relations among variables within a large dataset. The main aim of this learning algorithm is to find the dependency of one data item on another data item and map those variables accordingly so that it can generate maximum profit. This algorithm is mainly applied in Market Basket analysis, Web usage mining, continuous production, etc.

Some popular algorithms of Association rule learning are Apriori Algorithm, Eclat, FP-growth algorithm.

Advantages and Disadvantages of Unsupervised Learning Algorithm

Advantages:

• These algorithms can be used for complicated tasks compared to the supervised ones because these algorithms work on the unlabeled dataset.
• Unsupervised algorithms are preferable for various tasks as getting the unlabeled dataset is easier as compared to the labelled dataset.

Disadvantages:

• The output of an unsupervised algorithm can be less accurate as the dataset is not labelled, and algorithms are not trained with the exact output in prior.
• Working with Unsupervised learning is more difficult as it works with the unlabelled dataset that does not map with the output.

Applications of Unsupervised Learning

• Network Analysis: Unsupervised learning is used for identifying plagiarism and copyright in document network analysis of text data for scholarly articles.
• Recommendation Systems: Recommendation systems widely use unsupervised learning techniques for building recommendation applications for different web applications and e-commerce websites.
• Anomaly Detection: Anomaly detection is a popular application of unsupervised learning, which can identify unusual data points within the dataset. It is used to discover fraudulent trans
• Singular Value Decomposition: Singular Value Decomposition or SVD is used to extract particular information from the database. For example, extracting information of each user located at a particular location.

Semi-Supervised Learning

Semi-Supervised learning is a type of Machine Learning algorithm that lies between Supervised and Unsupervised machine learning. It represents the intermediate ground between Supervised (With Labelled training data) and Unsupervised learning (with no labelled training data) algorithms and uses the combination of labelled and unlabeled datasets during the training period.

Although Semi-supervised learning is the middle ground between supervised and unsupervised learning and operates on the data that consists of a few labels, it mostly consists of unlabeled data. As labels are costly, but for corporate purposes, they may have few labels. It is completely different from supervised and unsupervised learning as they are based on the presence & absence of labels.

To overcome the drawbacks of supervised learning and unsupervised learning algorithms, the concept of Semi-supervised learning is introduced. The main aim of semi-supervised learning is to effectively use all the available data, rather than only labelled data like in supervised learning. Initially, similar data is clustered along with an unsupervised learning algorithm, and further, it helps to label the unlabeled data into labelled data. It is because labelled data is a comparatively more expensive acquisition than unlabeled data.

We can imagine these algorithms with an example. Supervised learning is where a student is under the supervision of an instructor at home and college. Further, if that student is self-analyzing the same concept without any help from the instructor, it comes under unsupervised learning. Under semi-supervised learning, the student has to revise himself after analyzing the same concept under the guidance of an instructor at college.

Advantages and disadvantages of Semi-supervised Learning

Advantages:

• It is simple and easy to understand the algorithm.
• It is highly efficient.
• It is used to solve drawbacks of Supervised and Unsupervised Learning algorithms.

Disadvantages:

• Iterations results may not be stable.
• We cannot apply these algorithms to network-level data.
• Accuracy is low.

Reinforcement Learning

Reinforcement learning works on a feedback-based process, in which an AI agent (A software component) automatically explore its surrounding by hitting & trial, taking action, learning from experiences, and improving its performance. Agent gets rewarded for each good action and gets punished for each bad action; hence the goal of reinforcement learning agent is to maximize the rewards.

In reinforcement learning, there is no labelled data like supervised learning, and agents learn from their experiences only.

The reinforcement learning process is similar to a human being; for example, a child learns various things by experiences in his day-to-day life. An example of reinforcement learning is to play a game, where the Game is the environment, moves of an agent at each step define states, and the goal of the agent is to get a high score. Agent receives feedback in terms of punishment and rewards.

Due to its way of working, reinforcement learning is employed in different fields such as Game theory, Operation Research, Information theory, and multi-agent systems.

A reinforcement learning problem can be formalized using Markov Decision Process(MDP). In MDP, the agent constantly interacts with the environment and performs actions; at each action, the environment responds and generates a new state.

Categories of Reinforcement Learning

Reinforcement learning is categorized mainly into two types of methods/algorithms:

• Positive Reinforcement Learning: Positive reinforcement learning specifies increasing the tendency that the required behavior would occur again by adding something. It enhances the strength of the behavior of the agent and positively impacts it.
• Negative Reinforcement Learning: Negative reinforcement learning works exactly opposite to the positive RL. It increases the tendency that the specific behaviour would occur again by avoiding the negative condition.

Applications of Reinforcement Learning

• Video Games:
  RL algorithms are much popular in gaming applications. It is used to gain super-human performance. Some popular games that use RL algorithms are AlphaGO and AlphaGO Zero.
• Resource Management:
  The “Resource Management with Deep Reinforcement Learning” paper showed how to use RL in a computer to automatically learn and schedule resources to wait for different jobs in order to minimize average job slowdown.
• Robotics:
  RL is widely being used in Robotics applications. Robots are used in the industrial and manufacturing area, and these robots are made more powerful with reinforcement learning. There are different industries that have their vision of building intelligent robots using AI and Machine learning technology.
• Text Mining
  Text-mining, one of the great applications of NLP, is now being implemented with the help of Reinforcement Learning by Salesforce company.

Advantages and Disadvantages of Reinforcement Learning

Advantages

• It helps in solving complex real-world problems which are difficult to be solved by general techniques.
• The learning model of RL is similar to the learning of human beings; hence most accurate results can be found.
• Helps in achieving long-term results.

Disadvantage

• RL algorithms are not preferred for simple problems.
• RL algorithms require huge data and computations.
• Too much reinforcement learning can lead to an overload of states which can weaken the results.

The curse of dimensionality limits reinforcement learning for real physical systems.

Conclusion

This module highlighted the primary machine learning types, their workings, subcategories, regression in machine learning, classification in machine learning, clustering in machine learning, dimensionality reduction in machine learning, their use cases, and the advantages, and disadvantages of different types of Machine learning. In the next module, we will be discussing different aspects related to datasets for Machine Learning. See you there.

Machine learning is a type of AI that teaches machines how to learn, interpret and predict results based on a set of data. As the world — and internet — have grown exponentially in the past few years, machine learning processes have become common for organizations of all kinds. For example, companies in the healthcare sector use ML to detect and treat diseases better, while in the farming sector machine learning helps predict harvest yields.

ML involves computers finding insightful information without being told where to look, differing so from traditional computing, in which algorithms are sets of explicitly programmed instructions. ML does this by leveraging algorithms that are trained on data, on which they learn in an iterative process in order to generate outputs, and automate decision-making processes.

The three basic ingredient of machine learning

There are three basic functional ingredients of ML.

1. Data: The dataset you want to use must be well-structured, accurate. The data you use can be labeled or unlabeled. Unlabeled data are sample items — e.g. photos, videos, news articles — that don’t need additional explanation, while labeled ones are augmented: unlabeled information is bundled and an explanation, with a tag, is added to them.
2. Algorithm: there are different types of algorithms that can be used (e.g. linear regression, logistic regression). Choosing the right algorithm is both a combination of business need, specification, experimentation and time available.
3. Model: ​​A “model” is the output of a machine learning algorithm run on data. It represents the rules, numbers, and any other algorithm-specific data structures required to make predictions.

How is machine learning used

Successful machine learning algorithms can be used for a variety of purposes. The Director of the Massachusetts Institute of Technology (MIT), Thomas W. Malone wrote in a recent research:

“ The function of a machine learning system can be descriptive, meaning that the system uses the data to explain what happened; predictive, meaning the system uses the data to predict what will happen; or prescriptive, meaning the system will use the data to make suggestions about what action to take.”

What are training data

Training data is the initial data used to train machine learning models. Training datasets are fed to machine learning algorithms so that they can learn to make predictions, or perform a desired task. This type of data is key, because it helps machines achieve results and work in the right way, as shown in the graph below.

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The innovative power of machine learning models is in the fact that they learn and improve over time, as they are exposed to relevant training data. Some data is held out from the training data to be used as “evaluation data ‘’, which validates and tests how accurate the machine learning model is. This type of data is contained in the validation and test datasets which will be later discussed.

The importance of training data

Training data is a key part of the machine learning process. There are several aspects in play when you build a training dataset. The prime consideration is the size of datasets which depends on the use made of ML: More complicated the use, the bigger the size of the dataset. In the case of unsupervised learning, the more patterns you want your model to identify, the more examples it will need. You want a scalable learning algorithm, which can deal with any amount of data.

Second thing to consider is the quality of the data. Concerning this aspect, it is important to feed the system with carefully curated data. The higher the quality of your training data is, the better will your machine learning model be, especially in the early stages.

Having quality in data you used, means collecting real-world data, which closely mimics how an application will receive external inputs, and diverse data, for reducing the possibility of biases that we will later discuss.

To understand how much training data is important, think of vehicle manufacturers that are pivoting themselves towards the challenge of autonomous drive. The quality of the data is essential to ensuring autonomous vehicles operate safely and as expected. It isn’t enough for vehicles to perform well in simulated-good weather conditions, or on one type of road. They must perform flawlessly in all weather conditions in every imaginable road scenario.

Keep also in mind that the quality of the data comes from including the final user in your product/service. The most successful AI projects are those that integrate data collection during the product life-cycle. It must be built into the core of the product itself, in order that every time a user engages with it, you collect data from that interaction. The main purpose is to use the constant data flow to improve your offer for the user. Think of Spotify that uses an AI system called “collaborative filtering”, to create personalized “Discover Weekly” playlists which help fans to sort out new music that’s appealing to them. The more the user listens to and searches for music that he/she enjoys, the more the app will know what to recommend.

How machine learning can learn from data

Machine learning offers a number of different ways to learn from data:

• Supervised learning : it can be regarded as a “hands-on” approach, since it uses labeled data. Humans must tag, label, or annotate the data to their criteria, in order to train the model to predict the “correct” outputs which are predetermined.
• Unsupervised learning : it can be construed as a “broad pattern-seeking” approach, since it uses unlabeled data and, instead of predicting the correct output, models are tasked with finding patterns, similarities and deviations, that can be then applied to other data that exhibit similar behaviour.
• Reinforcement learning: it uses unlabeled data and it involves a feedback mechanism. When it performs a task correctly, it receives positive feedback, which strengthens the model in connecting the target inputs and output. Likewise, it can receive negative feedback for incorrect solutions.

Validation and testing

Validation and testing begins with splitting your training dataset. The “Valid-Test split” is a technique to evaluate the performance of your ML model. You need to split the data because you don’t want your model to over-learn from training data, to not perform well. But, most of all, you want to evaluate how well your model is generalizing.

Hence, you held back from training dataset, validation and testing subsets for assessing your model in a meaningful way. Notice that a typical split ratio of data, between training, validation and testing sets is around 50:25:25. A brief explanation of the role of each of these dataset is below.

• Validation dataset: it is useful when it comes to model selection. The data included in this set will be used to find the optimal values for the parameters of the model under consideration. When you work with ML models, you typically need to test multiple models with different parameters values for finding the optimal values that will give the best possible performance. Therefore, in order to pick the best model you must evaluate each of them.
• Testing dataset: when you have tuned the model by performing parameters optimisation, you should end up with the final model. The testing set is used to provide an unbiased evaluation of the performance of this model and ensure that it can generalise well to new, unseen data.

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Bias in machine learning

Bias in Machine Learning is defined as the phenomena of observing results that are systematically prejudiced due to faulty assumptions. It can be interpreted as the accuracy of our predictions. A high bias will result in an inaccurate prediction, so you need to know what bias is, to prevent it. An inaccurate prediction can derive from

There are techniques to handle bias, and they are related to the quality of training data. For sure, they must be as diverse as possible, including as many relevant groups of data as possible. The more inclusive is the dataset, the less likely it is to turn a blind eye to a given data group. You must identify representative data.

In general, bias reduces the potential of AI for business and society by encouraging mistrust and producing distorted results. Any value delivered by machine learning systems in terms of efficiency or productivity will be wiped out if the algorithms discriminate against individuals.

What are the different types of bias in Machine Learning

• Sample bias: if the sample data used to train models do not replicate a real-world scenario, models are exposed to a part of the problem space. An example is facial recognition softwares primarily trained on images of white men.
• Prejudicial bias: it occurs due to cultural stereotypes. Social status and gender may slide into a model. The consequence is that results will be skewed against people of a particular group. When a software used to hire talents, is fed mostly male resumes, it will learn that men are preferable to other profiles
• Algorithmic bias: it may occur when the algorithm used is inappropriate for the current application. It is an error that derives from an error of approach. This bias can emerge due to the wrong “architectural” design of the algorithm or to unintentional decisions relating to the way data is collected. It is quite difficult to address.
• Exclusion bias: it happens when important data are excluded from the training dataset. For example, imagine you have a dataset of customer sales in America and Europe. 98% of the customers are from America, so you choose to delete the location data thinking it is irrelevant. This means your model will not pick up on the fact that your European customers spend two times more.

How businesses are using machine learning

Every company is pivoting to use machine learning in their products and services in some way. It is almost like ML is becoming an expected feature. We are using it to make human tasks easier, faster, and better than before.

As said in the introduction, an example of Machine Learning applied to content consumption is Netflix with its personalisation of movie recommendations, in order to “learn to entertain the world”. Users who watch A are likely to watch B. Netflix uses the watching history of other users with similar tastes to recommend what you may be most interested in watching next.

Product recommendation is one of the most successful applications of machine learning in business. They will pull in front of you those products you are most likely to buy, according to the product you have previously bought and browsed. For example, Amazon uses the browsing history of a user to always keep those products in the customer’s sight.

Machine learning is also used by advertisers, for the so called “machine learning based advertising”. Using ML in this field is fundamental especially because of the changes introduced by Apple updates for iOS. Privacy is a key feature of them, and this made for marketers the optimization of the ROI of their campaigns even harder, since precise targeting has become difficult. As advertising gets more complex, you need to rely on the analytical and on real-time optimisation capacities that an algorithm can provide.

Here at Mapendo, Jenga, our proprietary AI technology, collects tens of thousands of data related to a given topic, finds patterns, and it manages to predict the possible outcome of a marketing campaign and finds the audience that is most likely to convert for a type of ad.

Our algorithm has been trained to optimize the traffic according to the client’s KPIs, maximize user retention and generate post install actions. Advertisers need to leverage technology to find meaningful insights, predict outcomes and maximise the efficiency of their investment, by choosing the right channels and budget.

Conclusion

A basic understanding of machine learning is important. This is for two reasons. The first one is that ML can really improve our life, finding applications in our daily routines. Instead, the second one is that with digitalisation disrupting every industry, sharing and delivering data has become a high priority.

As we have explained it is fundamental to build a trained model. For guaranteeing a quality “coaching”, you must provide machine learning with accurate data, and in the right amounts. The way you teach the algorithm and how it learns, depends on how much accuracy it is put into constructing your dataset, inputting labeled or unlabeled data, and paying close attention to not feed the algorithm with biased ones. Data biases will lead to unreliable results, and if you use those, you will give the wrong answer to your problem. Biased datasets can jeopardize business processes and decisions.

The last fundamental step is the one that leads to the final results of the machine learning process. Validation and, in greater detail, testing will determine the overall model performance, making sure that the model can really work when you use it to give an answer to a real-world problem.

In this blog on what is Machine Learning, you will learn about Machine Learning, the differences between AI and Machine Learning, why Machine Learning matters, applications of Machine Learning, Machine Learning languages, and some of the most common open-source Machine Learning tools.

The following topics will be covered in this blog:

• Machine Learning definition
• Why Machine Learning?
• How does Machine Learning work?
• What are the different types of Machine Learning?
• Machine Learning Algorithms and Processes
• ML Programming Languages
• Machine Learning Tools
• Difference between AI and Machine Learning
• Applications of Machine Learning
• Advantages and Disadvantages of Machine learning
• Scope of Machine Learning
• Prerequisites for Machine Learning
• Conclusion

Machine Learning Definition

Even though there are various Machine Learning examples or applications that we use in our daily lives, people still get confused about Machine Learning, so let’s start by looking at the Machine Learning definition.

In layman’s terms, Machine Learning can be defined as the ability of a machine to learn something without having to be programmed for that specific thing. It is the field of study where computers use a massive set of data and apply algorithms for ‘training’ themselves and making predictions. Training in Machine Learning entails feeding a lot of data into the algorithm and allowing the machine itself to learn more about the processed information.

Answering whether the animal in a photo is a cat or a dog, spotting obstacles in front of a self-driving car, spam mail detection, and speech recognition of a YouTube video to generate captions are just a few examples out of a plethora of predictive Machine Learning models.

Another Machine Learning definition can be given as Machine learning is a subset of Artificial Intelligence that comprises algorithms programmed to gather information without explicit instructions at each step. It has experienced the colossal success of late.

We have often seen confusion around the use of the words Artificial Intelligence and Machine Learning. They are very much related and often seem to be used interchangeably, yet both are different. Confused? Let us elaborate on AI vs. ML vs. DL.

Go through these Top 40 Machine Learning Interview Questions and Answers to crack your interviews.

Why Machine Learning?

Let us start with an instance where a machine surpasses in a strategic game by self-learning. In 2016, the strongest Go player (Go is an abstract strategy board game invented in China more than 2,500 years ago) in the world, Lee Sedol, sat down for a match against Google DeepMind’s Machine Learning program, AlphaGo. AlphaGo won the 5-day long match.

One thing to take away from this instance is not that a machine can learn to conquer Go, but the fact that the ways in which these revolutionary advances in Machine Learning—machines’ ability to mimic a human brain—can be applied are beyond imagination.

Machine Learning has paved its way into various business industries across the world. It is all because of the incredible ability of Machine Learning to drive organizational growth, automate manual and mundane jobs, enrich the customer experience, and meet business goals.

According to BCC Research, the global market for Machine Learning is expected to grow from $17.1 billion in 2021 to $90.1 billion by 2026 with a compound annual growth rate (CAGR) of 39.4% for the period of 2021-2026.

Moreover, Machine Learning Engineer is the fourth-fastest growing job as per LinkedIn.

Both Artificial Intelligence and Machine Learning are going to be imperative to the forthcoming society. Hence, this is the right time to learn Machine Learning.

Enroll for the Machine Learning Training in Noida now and land in your dream job!

How does Machine Learning work?

Machine learning works on different types of algorithms and techniques. These algorithms are created with the help of various ML programming languages. Usually, a training dataset is fed to the algorithm to create a model.

Now, whenever input is provided to the ML algorithm, it returns a result value/predictions based on the model. Now, if the prediction is accurate, it is accepted and the algorithm is deployed. But if the prediction is not accurate, the algorithm is trained repeatedly with a training dataset to arrive at an accurate prediction/result.

Consider this example:

If you wish to predict the weather patterns in a particular area, you can feed the past weather trends and patterns to the model through the algorithm. This will be the training dataset for the algorithm. Now if the model understands perfectly, the result will be accurate.

What are the different types of Machine Learning?

Machine Learning algorithms run on various programming languages and techniques. However, these algorithms are trained using various methods, out of which three main types of Machine learning are:

• Supervised Learning
• Unsupervised Learning
• Reinforcement Learning

Supervised Learning

Supervised Learning is the most basic type of Machine Learning, where labeled data is used for training the machine learning algorithms. A dataset is given to the ML model for understanding and solving the problem. This dataset is a smaller version of a larger dataset and conveys the basic idea of the problem to the machine learning algorithm.

Unsupervised Learning

Unsupervised Learning is the type of Machine Learning where no human intervention is required to make the data machine-readable and train the algorithm. Also, contrary to supervised learning, unlabeled data is used in the case of unsupervised learning.

Since there is no human intervention and unlabeled data is used, the algorithm can work on a larger data set. Unlike supervised learning, unsupervised learning does not require labels to establish relationships between two data points.

Reinforcement Learning

Reinforcement Learning is the type of Machine Learning where the algorithm works upon itself and learns from new situations by using a trial-and-error method. Whether the output is favorable or not is decided based on the output result already fed to each iteration.

Machine Learning Algorithms and Processes

Machine Learning algorithms are sets of instructions that the model follows to return an acceptable result or prediction. Basically, the algorithms analyze the data fed to them and establish a relationship between the variables and data points to return the result.

Over time, these algorithms learn to become more efficient and optimize the processes when new data is fed into the model. There are three main categories in which these algorithms are divided- Supervised Learning, Unsupervised Learning, and Reinforcement Learning. These have already been discussed in the above sections.

ML Programming Languages

Now, when it comes to the implementation of Machine Learning, it is important to have a knowledge of programming languages that a computer can understand. The most common programming languages used in Machine Learning are given below.

According to the GitHub 2021report, the below-given table ranks as the most popular programming language for Machine Learning in 2021:

Rank	Programming Language
1	JavaScript
2	Python
3	Java
4	Go
5	TypeScript
6	C++
7	Ruby
8	PHP
9	C#
10	C

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Machine Learning Tools

Machine Learning open-source tools are nothing but libraries used in programming languages like Python, R, C++, Java, Scala, Javascript, etc. to make the most out of Machine Learning algorithms.

• Keras: Keras is an open-source neural network library written in Python. It is capable of running on top of TensorFlow.
• PyTorch: PyTorch is an open-source Machine Learning library for Python, based on Torch, used for applications such as Natural Language Processing.
• TensorFlow: Created by the Google Brain team, TensorFlow is an open-source library for numerical computation and large-scale Machine Learning.
• Scikit-learn: Scikit-learn, also known as Sklearn, is a Python library that has become very popular for solving Science, Math, and Statistics problems–because of its easy-to-adopt nature and its wide range of applications in the field of Machine Learning.
• Shogun: Shogun can be used with Java, Python, R, Ruby, and MATLAB. It offers a wide range of efficient and unified Machine Learning methods.
• Spark MLlib: Spark MLlib is the Machine Learning library used in Apache Spark and Apache Hadoop. Although Java is the primary language for working in MLlib, Python users are also allowed to connect to MLlib through the NumPy library.

Difference between AI and Machine Learning

There seems to be a lack of a bright-line distinction between what Machine Learning is and what it is not. Moreover, everyone is using the labels ‘AI’ and ‘ML’ where they do not belong and that includes using the terms interchangeably.

Artificial Intelligence is not a machine or a system. It is a concept that is implemented on machines. When we talk about Artificial Intelligence, it could be making a machine move or it could be making a machine detect spam mail. For all these different implementations of AI, there are different sub-fields, and one such sub-field is Machine Learning. There are applications of Artificial Intelligence that are not related to Machine Learning. For example, symbolic logic: rules engines, expert systems, and knowledge graphs.

Machine Learning uses large sets of data and hours of training to make predictions on probable outcomes. But when Machine Learning ‘comes to life’ and moves beyond simple programming, and reflects and interacts with people even at the most basic level, AI comes into play.

AI is a step beyond Machine Learning, yet it needs ML to reflect and optimize decisions. AI uses what it has gained from ML to simulate intelligence, the same way a human is constantly observing their surrounding environment and making intelligent decisions. AI leads to intelligence or wisdom and its end goal is to simulate natural intelligence to solve complex problems of the world.

Now that we have gathered an idea of What Machine Learning is and the difference between AI and Machine Learning, let us move ahead and see why Machine Learning is important.

ARTIFICIAL INTELLIGENCE	MACHINE LEARNING
AI stands for Artificial intelligence, where intelligence is defined as acquisition of knowledge intelligence is defined as an ability to acquire and apply knowledge.	ML stands for Machine Learning which is defined as the acquisition of knowledge or skill
The aim is to increase the chance of success and not accuracy.	The aim is to increase accuracy, but it does not care about success
It work as a computer program that does smart work	Here, machine takes data and learn from data.
The goal is to simulate natural intelligence to solve complex problems.	The goal is to learn from data on certain tasks to maximize the performance on that task.
AI is decision making.	ML allows systems to learn new things from data.
It is developing a system which mimics humans to solve problems.	It involves creating self learning algorithms.
AI will go for finding the optimal solution.	ML will go for a solution whether it is optimal or not.
AI leads to intelligence or wisdom.	ML leads to knowledge.
AI is a broader family consisting of ML and DL as its components.	ML is a subset of AI.

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Applications of Machine Learning

As mentioned earlier, the human race has already stepped into the future world with machines. The pervasive growth of Machine Learning can be seen in almost every other field. Let me list out a few real-life applications of Machine Learning.

Fraud Detection

Fraud detection refers to the act of illicitly drawing out money from people by deceiving them. Machine Learning can go a long way in decreasing instances of fraud detection and save many individuals and organizations from losing their money.

For example- by feeding an algorithm into the model, spam emails can be easily detected. Also, the right machine learning models can easily detect fraudulent transactions or suspicious online banking activities.

In fact, fraud detection ML algorithms are nowadays being considered as much more effective than humans.

Moley’s Robotic Kitchen

Machine Learning can do wonders in the food and beverage industry too. Consider this example- The kitchen comes up with a pair of robotic arms, an oven, a shelf for food and utensils, and a touch screen.

Moley’s kitchen is a gift of Machine Learning: it will learn n number of recipes for you, will cook with remarkable precision, and will also clean up by itself. It sounds great, doesn’t it?

Netflix Movie Recommendation

The algorithm that Netflix uses to recommend movies is nothing but Machine Learning. More than 80 percent of the shows and movies are discovered through the recommendation section.

To recommend movies, it goes through threads within the content rather than relying on the genre board in order to make predictions. According to Todd Yellin, VP of Product at Netflix, the Machine Learning algorithm is one of the pillars of Netflix.

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Alexa

The latest innovations of Amazon have the brain and the voice of Alexa. Now, for those who are not aware of Alexa, it is the voice-controlled Amazon ‘personal assistant’ in Amazon Echo devices.

Alexa can play music, provide information, deliver news and sports scores, tell you the weather, control your smart home, and even allow prime members to order products that they’ve ordered before. Alexa is smart and gets updated through the Cloud and learns all the time, by itself.

But, does Alexa understand commands? How does it learn by itself? Everything is a gift of the Machine Learning algorithm.

Amazon Product Recommendation

We are sure that you might have noticed while buying something online from Amazon, it recommends a set of items that are bought together or items that are often bought together, along with your ordered item.

Have you ever wondered how Amazon makes those recommendations? Well again, Amazon uses the Machine Learning algorithm to do so.

Google Maps

How does Google Maps predict traffic on a particular route? How does it tell you the estimated time for a certain trip?

Google Maps anonymously sends real-time data from the Google Maps users on the same route back to Google. Google uses the Machine Learning algorithm on this data to accurately predict the traffic on that route.

These are some of the Machine Learning examples that we see or use in our daily lives. Let us go ahead and discuss how we can implement a Machine Learning algorithm.

Come to Intellipaat’s Machine Learning Community if you have more queries on Machine Learning!

Advantages and Disadvantages of Machine Learning

• Easily identifies trends and patterns

Machine Learning can review large volumes of data and discover specific trends and patterns that would not be apparent to humans. For instance, for e-commerce websites like Amazon and Flipkart, it serves to understand the browsing behaviors and purchase histories of its users to help cater to the right products, deals, and reminders relevant to them. It uses the results to reveal relevant advertisements to them.

• Continuous Improvement

We are continuously generating new data and when we provide this data to the Machine Learning model which helps it to upgrade with time and increase its performance and accuracy. We can say it is like gaining experience as they keep improving in accuracy and efficiency. This lets them make better decisions.

• Handling multidimensional and multi-variety data

Machine Learning algorithms are good at handling data that are multidimensional and multi-variety, and they can do this in dynamic or uncertain environments.

•  Wide Applications

You could be an e-tailer or a healthcare provider and make Machine Learning work for you. Where it does apply, it holds the capability to help deliver a much more personal experience to customers while also targeting the right customers.

Disadvantages of Machine Learning

• Data Acquisition

Machine Learning requires a massive amount of data sets to train on, and these should be inclusive/unbiased, and of good quality. There can also be times where we must wait for new data to be generated.

• Time and Resources

Machine Learning needs enough time to let the algorithms learn and develop enough to fulfill their purpose with a considerable amount of accuracy and relevancy. It also needs massive resources to function. This can mean additional requirements of computer power for you.

• Interpretation of Results

Another major challenge is the ability to accurately interpret results generated by the algorithms. You must also carefully choose the algorithms for your purpose. Sometimes, based on some analysis you might select an algorithm but it is not necessary that this model is best for the problem.

• High error-susceptibility

Machine Learning is autonomous but highly susceptible to errors. Suppose you train an algorithm with data sets small enough to not be inclusive. You end up with biased predictions coming from a biased training set. This leads to irrelevant advertisements being displayed to customers. In the case of Machine Learning, such blunders can set off a chain of errors that can go undetected for long periods of time. And when they do get noticed, it takes quite some time to recognize the source of the issue, and even longer to correct it.

Scope of Machine Learning

The scope of Machine Learning covers varied industries and sectors. It is expanding across all fields such as Banking and Finance, Information Technology, Media & Entertainment, Gaming, and the Automotive industry. As the Machine Learning scope is very high, there are some areas where researchers are working toward revolutionizing the world for the future.

The scope of Machine Learning in India, as well as in other parts of the world, is high in comparison to other career fields when it comes to job opportunities.

According to Gartner, there will be 2.3 million jobs in the field of Artificial Intelligence and Machine Learning by 2023. Also, the salary of a Machine Learning Engineer is much higher than the salaries offered to other job profiles. According to Forbes, the average salary of an ML Engineer in the United States is US$99,007.

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Prerequisites for Machine Learning

Prerequisites to building a career in Machine Learning include knowledge of the following:

• Statistics– Knowledge of statistical tools and techniques is a basic requirement to understand Machine Learning. You should be well trained in using various types of statistics such as descriptive statistics and inferential statistics to extract useful information from raw data.
• Probability– Machine Learning is built on probability. The very possibility of the occurrence of an event is known as probability.
• Programming languages– It is very important that an ML engineer knows which machine-readable programming language to be used.
• Calculus– The working of Machine Learning algorithms depends on how Calculus and related concepts such as Integration and Differentiation are used. Hence, it is very important that you understand and are well acquainted with Calculus.
• Linear Algebra– Vectors, Matrices, and Linear Transformations form an important part of Linear Algebra and play an important role in dataset operations.

Conclusion

This module focuses on the meaning of Machine Learning, common Machine Learning definitions, the difference between AI and Machine Learning, why Machine Learning matters, prerequisites, and types of machine learning. We have also highlighted different Machine Learning tools, as well as discussed some of the applications of Machine Learning. If you want to have a deeper understanding of Machine Learning, refer to the Machine Learning tutorial. See you there!

What is Machine Learning?

A subset of artificial intelligence (AI) and computer science, machine learning (ML) deals with the study and use of data and algorithms that mimic how humans learn. This helps machines gradually improve their accuracy. ML allows software applications to improve their prediction accuracy without being specifically programmed to do so. It estimates new output values by using historical data as input.

Importance of Machine Learning

In today’s technological era, machine learning has become an integral part of diverse industries and sectors. It is extremely important because it provides organisations with insights into trends in customer behaviour and business operating patterns, as well as assisting in the creation of new products. Machine learning is fundamental to the operations of many of today’s biggest organisations, like Facebook, Google, and Uber. For many businesses across the world, it has become a crucial competitive differentiator.

Types of Machine Learning

Machine learning algorithms are broadly divided into four types – supervised learning, unsupervised learning, semi-supervised learning, and reinforcement learning.

Supervised Learning    

In the case of supervised learning, machines are trained by examples. The machine is taught by example in supervised learning. The operator gives the machine learning algorithm a predefined dataset with specified inputs and outputs, and the algorithm needs to figure out how to get those inputs and outputs. While the operator is aware of the proper responses, the algorithm recognises patterns in data, learns from observations, and produces predictions. The algorithm predicts and is corrected by the operator; this process is repeated till the algorithm reaches a high level of precision.

Unsupervised Learning

The machine learning programme examines the data to detect trends. There is no response key or human interference to provide guidance. Instead, the machine analyses available data to discover correlations and linkages. The machine learning algorithm is left to evaluate massive data sets and address that data in an unsupervised learning process. The programme attempts to organise the data in a way that describes its structure. This could imply organizing the data into clusters or structuring it in a more organised manner. As it evaluates additional data, its capability to make decisions based on that data increases and gets more refined.

Semi-Supervised Learning        

It is akin to supervised learning but it employs both labelled and unlabelled data. Labelled data is mainly information that has semantic tags so that the algorithm can interpret it, but unlabelled data does not have that information. Machine learning systems can learn to categorise unlabelled data using this combination.

Reinforcement Learning

Reinforcement learning is concerned with regimented learning procedures in which a machine learning algorithm is given a set of actions, variables, and end values to follow. Following the definition of the rules, the algorithm attempts to explore several options and prospects, monitoring and assessing each output to determine which is ideal. Reinforcement learning instructs the machine through trial and error. It learns from previous experiences and begins to change its approach to the situation to reach the best possible outcome.

Applications of Machine Learning

Some of the applications of machine learning include:

• Recommendation engines
• Business process automation
• Spam filtering
• Malware threat detection
• Predictive maintenance
• Virtual personal assistant
• Medical diagnosis
• Stock market trading
• Speech and image recognition
• Self-driving cars

And the list goes on and on. Today, machine learning is at the heart of many real-world applications.

There are many institutions such as the FORE School of Management that are offering machine learning courses in Delhi NCR. The future of machine learning is promising and if you want to make a career in this domain, enrol in a course today to obtain a valid credential.

oday, most of the businesses rely on machine learning (ML) applications to understand revenue opportunities, identify market trends, predict customer behavior and pricing fluctuations as well as take the right business decisions. Developing these machine learning applications require following diligent planning and steps. Problem framing, data cleaning, feature engineering, model training, and improving model accuracy are a few of the steps that can be followed for developing machine learning applications.

Machine learning being a subset of artificial intelligence technology helps make sense out of historical data as well as helps in decision making. Machine learning is a technique set to find patterns in data and build mathematical models around those findings.

Once we build and train a machine learning algorithm to form a mathematical representation of these data, we can use that model to predict future data. For example, in retail, based on historical purchase data, we can predict whether a user will buy a particular product or not using a learned algorithm.

Types of machine learning algorithms

A machine learning algorithm can be divided into three categories:

1. Supervised machine learning
2. Unsupervised machine learning
3. Reinforcement machine learning

In businesses, we mostly use supervised machine learning algorithms for performing tasks like categorical classification (binary and multiclass), activity monitoring, predicting a numerical value, and a lot more. We also use unsupervised machine learning techniques for a few applications like grouping or clustering, dimensionality reduction, and anomaly detection.

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While both these approaches have many practical implications for businesses, reinforcement learning (RL) has a very limited business application like path optimization for the transit industry. However, RL is going through extensive research and slowly take over supervised and unsupervised learning. And believe me, RL holds the future for businesses a lot and is super powerful.

A case in point

Why is reinforcement learning so powerful?

Here is a story of AlphaGo and AlphaGoZero.

Go is the world’s oldest board game. It is so complex that if you calculate all the combination from the empty board, it will have combinations of more than the total number of particles in the universe.

DeepMind built AlphaGo algorithm based on reinforcement algorithms that learned by analyzing games and playing against a real player. In Oct 2015, it won against a professional player named Fan Hui by 5-0.

In March 2016, AlphaGo was set to take on the Go champion named Lee Sedol. Every Go expert was sure that it would be very easy for Lee Sedol to beat AlphaGo by 5-0.

Deep Mind invited Fan Hui again to check how good AlphaGo became a trained player with reinforcement learning algorithms at that time and how much it had improved. During the inspection, Fan Hui found a major weakness in AlphaGo, but there was no time to correct it.

To everyone’s surprise, AlphaGo won the game by 4-1.  Lee got a clue about the weakness of AlphaGo and won the fourth round against AlphaGo. However, AlphaGo improved its ability with only one game and won the fifth round against Lee despite its weakness.

AlphaGo was taught the Go game using video feed. The next version named AlphaGoZero learned the game just by playing against itself and feeding basic rules. In just three days of training, it surpassed the ability of AlphaGo, which won against the world champion Lee Sedol.

Although this was achieved by reinforcement learning, inside it, they used deep convolutional neural networks (CNN) to process images. CNN is the type of deep learning algorithms that are widely used in business applications.

When to use machine learning

Machine learning is a powerful tool, but it should not be used frequently for it is computationally extensive and needs training and updating of models on a regular basis. It is sometimes better to rely on conventional software than machine learning.

For certain use cases, we can build a robust solution without machine learning, which can rely on rules, simple calculations or pre-determined processes for results and decision-making. These things are easily programmable and do not need any exhaustive learning. Hence, experts suggest using machine learning in certain special cases and scenarios:

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There are two scenarios where we can use machine learning solutions:

1. Inability to code the rules:

• Tasks which cannot be done by deploying a set of rules
• Difficulty identifying and implementing rules
• Multiple rules to go hand in hand, which are difficult to code
• Other factors making it difficult to code the rules based on those factors
• Overlapping rules rendering inaccurate codes

2. Data scale is high:

• When you can define rules from a few samples, but it is difficult to scan millions of data sets for a better prediction.

Machine learning can be used for both the above scenarios as it brings out a mathematical model containing rules and can solve large-scale problems.

Steps for developing machine learning applications

Building a machine learning application is an iterative process and follows a set of sequences. Below are the steps involved in for developing machine learning applications:

Problem framing

This first step is to frame a machine learning problem in terms of what we want to predict and what kind of observation data we have to make those predictions. Predictions are generally a label or a target answer; it may be a yes/no label (binary classification) or a category (multiclass classification) or a real number (regression).

Collect and clean the data

Once we frame the problem and identify what kind of historical data we have for prediction modeling, the next step is to collect the data from a historical database or from open datasets or from any other data sources.

Not all the collected data is useful for a machine learning application. We may need to clean the irrelevant data, which may affect the accuracy of prediction or may take additional computation without aiding in the result.

Prepare data for ML application

Once the data ready for the machine learning algorithm, we need to transform the data in the form that the ML system can understand. Machines cannot understand an image or text. We need to convert it into numbers. It also requires building data pipeline depending on the machine learning application needs.

Feature engineering

Sometimes a raw data may not reveal all the facts about the targeted label. Feature engineering is a technique to create additional features combining two or more existing features with an arithmetic operation that is more relevant and sensible.

For example: In a compute engine, it is common for RAM and CPU usage to reach 95%, but something is messy when RAM usage is at 5% and CPU is at 93%. We can use a ration of RAM to CPU usage as a new feature, which may provide a better prediction. If we are using deep learning, it will automatically build features itself; we do not need explicit feature engineering.

Training a model

Before we train the model, we need to split the data into training and evaluation sets, as we need to monitor how well a model generalizes to unseen data. Now, the algorithm will learn the pattern and mapping between the feature and the label.

The learning can be linear or non-linear depending upon the activation function and algorithm. There are a few hyper parameters that affect the learning as well as training time such as learning rate, regularization, batch size, number of passes (epoch), optimization algorithm, and more.

Evaluating and improving model accuracy

Accuracy is a measure to know how good or bad a model is doing on an unseen validation set. Based on the current learnings, we need to evaluate how a model is doing on a validation set. Depending on the application, we can use different accuracy metrics. For e.g. for classification we may use, precision and recall or F1 Score; for object detection, we may use IoU (interaction over union).

If a model is not doing well, we may classify the problem in either of class 1) over-fitting and 2) under-fitting.

When a model is doing well on the training data, but not on the validation data, it is the over-fitting scenario. Somehow model is not generalizing well. The solution for the problem includes regularizing algorithm, decreasing input features, eliminating the redundant feature, and using resampling techniques like k-fold cross-validation.

In the under-fitting scenario, a model does poor on both training and validation dataset. The solution to this may include training with more data, evaluating different algorithms or architectures, using more number of passes, experimenting with learning rate or optimization algorithm.

After an iterative training, the algorithm will learn a model to represent those labels from input data and this model can be used to predict on the unseen data.

Serving with a model in production

After training, the model will do well on the unseen data and now it can be used for prediction. This is the most important thing for businesses. This is also one of the most difficult phases for business-oriented machine learning applications. In this phase, we deploy the model in production for the prediction on real-world data to derive the results.

Wrapping up

Machine learning is the enabler technology, but if we do not follow a proper plan and execution for training and learning of models on algorithms, we may fail. Hence, it is always a great idea for businesses that want to build complex machine learning systems to hire AI and Machine learning service providers and focus on their core competency.

eInfochips provides Artificial Intelligence & Machine Learning offerings to help organizations build highly-customized solutions running on advanced machine learning algorithms. We help companies integrate these algorithms with image & video analytics, as well as with emerging technologies such as augmented reality & virtual reality to deliver utmost customer satisfaction and gain a competitive edge over others. Know more about our machine learning expertise.

This article introduces the basics of machine learning theory, laying down the common concepts and techniques involved. This post is intended for the people starting with machine learning, making it easy to follow the core concepts and get comfortable with machine learning basics.

What is Machine Learning?

In 1959, Arthur Samuel, a computer scientist who pioneered the study of artificial intelligence, described machine learning as “the study that gives computers the ability to learn without being explicitly programmed.”

Alan Turing’s seminal paper (Turing, 1950) introduced a benchmark standard for demonstrating machine intelligence, such that a machine has to be intelligent and responsive in a manner that cannot be differentiated from that of a human being.

Machine Learning is an application of artificial intelligence where a computer/machine learns from the past experiences (input data) and makes future predictions. The performance of such a system should be at least human level.

A more technical definition given by Tom M. Mitchell’s (1997) : “A computer program is said to learn from experience E with respect to some class of tasks T and performance measure P, if its performance at tasks in T, as measured by P, improves with experience E.” Example:

A handwriting recognition learning problem:Task T: recognizing and classifying handwritten words within images
Performance measure P: percent of words correctly classified, accuracy
Training experience E: a data-set of handwritten words with given classifications

In order to perform the task T, the system learns from the data-set provided. A data-set is a collection of many examples. An example is a collection of features.

Machine Learning Categories

Machine Learning is generally categorized into three types: Supervised Learning, Unsupervised Learning, Reinforcement learning

Supervised Learning:

In supervised learning the machine experiences the examples along with the labels or targets for each example. The labels in the data help the algorithm to correlate the features.

Two of the most common supervised machine learning tasks are classification and regression.

In classification problems the machine must learn to predict discrete values. That is, the machine must predict the most probable category, class, or label for new examples. Applications of classification include predicting whether a stock's price will rise or fall, or deciding if a news article belongs to the politics or leisure section. In regression problems the machine must predict the value of a continuous response variable. Examples of regression problems include predicting the sales for a new product, or the salary for a job based on its description.

Unsupervised Learning:

When we have unclassified and unlabeled data, the system attempts to uncover patterns from the data . There is no label or target given for the examples. One common task is to group similar examples together called clustering.

Reinforcement Learning:

Reinforcement learning refers to goal-oriented algorithms, which learn how to attain a complex objective (goal) or maximize along a particular dimension over many steps. This method allows machines and software agents to automatically determine the ideal behavior within a specific context in order to maximize its performance. Simple reward feedback is required for the agent to learn which action is best; this is known as the reinforcement signal. For example, maximize the points won in a game over many moves.

Techniques of Supervised Machine Learning

Regression is a technique used to predict the value of a response (dependent) variables, from one or more predictor (independent) variables.

Most commonly used regressions techniques are: Linear Regression and Logistic Regression. We will discuss the theory behind these two prominent techniques alongside explaining many other key concepts like Gradient-descent algorithm, Over-fit/Under-fit, Error analysis, Regularization, Hyper-parameters, Cross-validation techniques involved in machine learning.

Linear Regression

In linear regression problems, the goal is to predict a real-value variable y from a given pattern X. In the case of linear regression the output is a linear function of the input. Letŷ be the output our model predicts: ŷ = WX+b

Here X is a vector (features of an example), W are the weights (vector of parameters) that determine how each feature affects the prediction andb is bias term. So our task T is to predict y from X, now we need to measure performance P to know how well the model performs.

Now to calculate the performance of the model, we first calculate the error of each example i as:

we take the absolute value of the error to take into account both positive and negative values of error.

Finally we calculate the mean for all recorded absolute errors (Average sum of all absolute errors).

Mean Absolute Error (MAE) = Average of All absolute errors

More popular way of measuring model performance is using

Mean Squared Error (MSE): Average of squared differences between prediction and actual observation.

The mean is halved (1/2) as a convenience for the computation of the gradient descent [discussed later], as the derivative term of the square function will cancel out the 1/2 term. For more discussion on the MAE vs MSE please refer [1] & [2].

The main aim of training the ML algorithm is to adjust the weights W to reduce the MAE or MSE.

To minimize the error, the model while experiencing the examples of the training set, updates the model parameters W. These error calculations when plotted against the W is also called cost function J(w), since it determines the cost/penalty of the model. So minimizing the error is also called as minimization the cost function J.

Gradient descent Algorithm:

When we plot the cost function J(w) vs w. It is represented as below:

As we see from the curve, there exists a value of parameters W which has the minimum cost Jmin. Now we need to find a way to reach this minimum cost.

In the gradient descent algorithm, we start with random model parameters and calculate the error for each learning iteration, keep updating the model parameters to move closer to the values that results in minimum cost.

repeat until minimum cost: {

}

In the above equation we are updating the model parameters after each iteration. The second term of the equation calculates the slope or gradient of the curve at each iteration.

The gradient of the cost function is calculated as partial derivative of cost function J with respect to each model parameter wj, j takes value of number of features [1 to n]. α, alpha, is the learning rate, or how quickly we want to move towards the minimum. If α is too large, we can overshoot. If α is too small, means small steps of learning hence the overall time taken by the model to observe all examples will be more.

There are three ways of doing gradient descent:

Batch gradient descent: Uses all of the training instances to update the model parameters in each iteration.

Mini-batch Gradient Descent: Instead of using all examples, Mini-batch Gradient Descent divides the training set into smaller size called batch denoted by ‘b’. Thus a mini-batch ‘b’ is used to update the model parameters in each iteration.

Stochastic Gradient Descent (SGD): updates the parameters using only a single training instance in each iteration. The training instance is usually selected randomly. Stochastic gradient descent is often preferred to optimize cost functions when there are hundreds of thousands of training instances or more, as it will converge more quickly than batch gradient descent [3].

Logistic Regression

In some problems the response variable is not normally distributed. For instance, a coin toss can result in two outcomes: heads or tails. The Bernoulli distribution describes the probability distribution of a random variable that can take the positive case with probability P or the negative case with probability 1-P. If the response variable represents a probability, it must be constrained to the range {0,1}.

In logistic regression, the response variable describes the probability that the outcome is the positive case. If the response variable is equal to or exceeds a discrimination threshold, the positive class is predicted; otherwise, the negative class is predicted.

The response variable is modeled as a function of a linear combination of the input variables using the logistic function.

Since our hypotheses ŷ has to satisfy 0 ≤ ŷ ≤ 1, this can be accomplished by plugging logistic function or “Sigmoid Function”

The function g(z) maps any real number to the (0, 1) interval, making it useful for transforming an arbitrary-valued function into a function better suited for classification. The following is a plot of the value of the sigmoid function for the range {-6,6}:

Now coming back to our logistic regression problem, Let us assume that z is a linear function of a single explanatory variable x. We can then express z as follows:

And the logistic function can now be written as:

Note that g(x) is interpreted as the probability of the dependent variable.
g(x) = 0.7, gives us a probability of 70% that our output is 1. Our probability that our prediction is 0 is just the complement of our probability that it is 1 (e.g. if probability that it is 1 is 70%, then the probability that it is 0 is 30%).

The input to the sigmoid function ‘g’ doesn’t need to be linear function. It can very well be a circle or any shape.

Cost Function

We cannot use the same cost function that we used for linear regression because the Sigmoid Function will cause the output to be wavy, causing many local optima. In other words, it will not be a convex function.

In order to ensure the cost function is convex (and therefore ensure convergence to the global minimum), the cost function is transformed using the logarithm of the sigmoid function. The cost function for logistic regression looks like:

Which can be written as:

So the cost function for logistic regression is:

Since the cost function is a convex function, we can run the gradient descent algorithm to find the minimum cost.

Under-fitting & Over-fitting

We try to make the machine learning algorithm fit the input data by increasing or decreasing the models capacity. In linear regression problems, we increase or decrease the degree of the polynomials.

Consider the problem of predicting y from x ∈ R. The leftmost figure below shows the result of fitting a line to a data-set. Since the data doesn’t lie in a straight line, so fit is not very good (left side figure).

To increase model capacity, we add another feature by adding term x² to it. This produces a better fit ( middle figure). But if we keep on doing so ( x⁵, 5th order polynomial, figure on the right side), we may be able to better fit the data but will not generalize well for new data. The first figure represents under-fitting and the last figure represents over-fitting.

Under-fitting:

When the model has fewer features and hence not able to learn from the data very well. This model has high bias.

Over-fitting:

When the model has complex functions and hence able to fit the data very well but is not able to generalize to predict new data. This model has high variance.

There are three main options to address the issue of over-fitting:

1. Reduce the number of features: Manually select which features to keep. Doing so, we may miss some important information, if we throw away some features.
2. Regularization: Keep all the features, but reduce the magnitude of weights W. Regularization works well when we have a lot of slightly useful feature.
3. Early stopping: When we are training a learning algorithm iteratively such as using gradient descent, we can measure how well each iteration of the model performs. Up to a certain number of iterations, each iteration improves the model. After that point, however, the model’s ability to generalize can weaken as it begins to over-fit the training data.

Regularization

Regularization can be applied to both linear and logistic regression by adding a penalty term to the error function in order to discourage the coefficients or weights from reaching large values.

Linear Regression with Regularization

The simplest such penalty term takes the form of a sum of squares of all of the coefficients, leading to a modified linear regression error function:

where lambda is our regularization parameter.

Now in order to minimize the error, we use gradient descent algorithm. We keep updating the model parameters to move closer to the values that results in minimum cost.

repeat until convergence ( with regularization): {

}

With some manipulation the above equation can also be represented as:

The first term in the above equation,

will always be less than 1. Intuitively you can see it as reducing the value of the coefficient by some amount on every update.

Logistic Regression with Regularization

The cost function of the logistic regression with Regularization is:

repeat until convergence ( with regularization): {

}

L1 and L2 Regularization

The regularization term used in the previous equations is called L2 or Ridge regularization.

The L2 penalty aims to minimize the squared magnitude of the weights.

There is another regularization called L1 or Lasso:

The L1 penalty aims to minimize the absolute value of the weights

Difference between L1 and L2
L2 shrinks all the coefficient by the same proportions but eliminates none, while L1 can shrink some coefficients to zero, thus performing feature selection. For more details read this.

Hyper-parameters

Hyper-parameters are “higher-level” parameters that describe structural information about a model that must be decided before fitting model parameters, examples of hyper-parameters we discussed so far:
Learning rate alpha , Regularization lambda.

Cross-Validation

The process to select the optimal values of hyper-parameters is called model selection. if we reuse the same test data-set over and over again during model selection, it will become part of our training data and thus the model will be more likely to over fit.

The overall data set is divided into:

1. the training data set
2. validation data set
3. test data set.

The training set is used to fit the different models, and the performance on the validation set is then used for the model selection. The advantage of keeping a test set that the model hasn’t seen before during the training and model selection steps is that we avoid over-fitting the model and the model is able to better generalize to unseen data.

In many applications, however, the supply of data for training and testing will be limited, and in order to build good models, we wish to use as much of the available data as possible for training. However, if the validation set is small, it will give a relatively noisy estimate of predictive performance. One solution to this dilemma is to use cross-validation, which is illustrated in Figure below.

Below Cross-validation steps are taken from here, adding here for completeness.

Cross-Validation Step-by-Step:

These are the steps for selecting hyper-parameters using K-fold cross-validation:

1. Split your training data into K = 4 equal parts, or “folds.”
2. Choose a set of hyper-parameters, you wish to optimize.
3. Train your model with that set of hyper-parameters on the first 3 folds.
4. Evaluate it on the 4th fold, or the”hold-out” fold.
5. Repeat steps (3) and (4) K (4) times with the same set of hyper-parameters, each time holding out a different fold.
6. Aggregate the performance across all 4 folds. This is your performance metric for the set of hyper-parameters.
7. Repeat steps (2) to (6) for all sets of hyper-parameters you wish to consider.

Cross-validation allows us to tune hyper-parameters with only our training set. This allows us to keep the test set as a truly unseen data-set for selecting final model.

Conclusion

We’ve covered some of the key concepts in the field of Machine Learning, starting with the definition of machine learning and then covering different types of machine learning techniques. We discussed the theory behind the most common regression techniques (Linear and Logistic) alongside discussed other key concepts of machine learning.

Machines’ current ability to learn is present in many aspects of everyday life. Machine learning is behind the recommendations for movies we receive on digital platforms, virtual assistants’ ability to recognize speech, or self-driving cars’ ability to see the road. But its origin as a branch of artificial intelligence dates began several decades ago. Why is this technology so important now, and what makes it so revolutionary?

Machine learning, or automated learning, is a branch of artificial intelligence that allows machines to learn without being programmed for this specific purpose. An essential skill to make systems that are not only smart, but autonomous, and capable of identifying patterns in the data to convert them into predictions. This technology is currently present in an endless number of applications, such as the Netflix and Spotify recommendations, Gmail’s smart responses or Alexa and Siri’s natural speech.

BIG DATA

Artificial Intelligence: the challenge of turning data into tangible value

Representatives of three leading organizations – Google, Telefónica and BBVA – spoke during the Open Summit event about the different applications of artificial intelligence (AI) in the business world and how to extract tangible value from data for people and for society as a whole, at a scale and in a structured way.

“Ultimately, machine learning is a master at pattern recognition, and is able to convert a data sample into a computer program that can extract interferences from new data sets it has not been previously trained for,” explains José Luis Espinoza, data scientist at BBVA Mexico. This ability to learn is also used to improve search engines, robotics, medical diagnosis or even fraud detection for credit cards.

Although now is the time when this discipline is getting headlines thanks to its ability to beat Go players or solve Rubik cubes, its origin dates back to the last century. “Without a doubt, statistics are the fundamental foundation of automated learning, which basically consists of a series of algorithms capable of analyzing large amounts of data to deduct the best result for a certain problem,” adds Espinoza.

Old math, new computing

We have to go back to the 19th century to find of the mathematical challenges that set the stage for this technology. For example, Bayes’ theorem (1812) defined the probability of an event occurring based on knowledge of the previous conditions that could be related to this event. Years later, in the 1940s, another group of scientists laid the foundation for computer programming, capable of translating a series of instructions into actions that a computer could execute. These precedents made it possible for the mathematician Alan Turing, in 1950, to ask himself the question of whether it is possible for machines to think. This planted the seed for the creation of computers with artificial intelligence that are capable of autonomously replicating tasks that are typically performed by humans, such as writing or image recognition.

It was a little later, in the 1950s and 1960s, when different scientists started to investigate how to apply the human brain neural network’s biology to attempt to create the first smart machines. The idea came from the creation of artificial neural networks, a computing model inspired in the way neurons transmit information to each other through a network of interconnected nodes. One of the first experiments in this regard was conducted by Marvin Minksy and Dean Edmonds, scientists from the Massachusetts Institute of Technology (MIT), who managed to create a computer program capable of learning from experience to find its way out of a maze.

“Machine learning is a master at pattern recognition”

This was the first machine capable of learning to accomplish a task on its own, without being explicitly programmed for this purpose. Instead, it did so by learning from examples provided at the outset. The accomplishment represented a paradigm shift from the broader concept of artificial intelligence. “Machine learning’s great milestone was that it made it possible to go from programming through rules to allowing the model to make these rules emerge unassisted thanks to data,” explains Juan Murillo, BBVA’s Data Strategy Manager.

Despite the success of the experiment, the accomplishment also demonstrated the limits that the technology had at the time. The lack of data available and the lack of computing power at the time meant that these systems did not have sufficient capacity to solve complex problems. This led to the arrival of the so-called “first artificial intelligence winter” – several decades when the lack of results and advances led scholars to lose hope for this discipline.

The rebirth of AI

The panorama started to change at the end of the 20th Century with the arrival of the Internet, the massive volumes of data available to train models, and computers’ growing computing power. “Now we can do the same thing as before, but a billion times faster. The algorithms can test the same combination of data 500 billion times to give us the optimal result in a matter of hours or minutes, when it used to take weeks or months,” says Espinoza.

In 1997, a famous milestone marked the rebirth of automated learning: the IBM Deep Blue system, which is trained from watching thousands of successful chess matches, managed to beat the world champion, Garry Kasparov. This accomplishment was possible thanks to deep learning, a subcategory of machine learning described for the first time in 1960, which allows systems to not only learn from experience, but to be capable of training themselves do so better and better using data. This milestone was possible then – and not 30 years before – thanks to the growing availability of data to train the model: “What this system did was statistically calculate which move had more probabilities of winning the game based on thousands of examples of matches previously watched,” adds Espinoza.

“The ability to adapt to changes in the data as they occur in the system was missing from previous techniques”

This technology has advanced exponentially in the past 20 years, and is also responsible for AlphaGo, the program capable of beating any human player at the game Go. And what is even more important: of training itself by constantly playing against itself to continue improving.

The system that AlphaGo uses to do this, in particular, is reinforcement learning, which is one of the three major trends currently used to train these models:

• Reinforcement learning takes place when a machine learns through trial and error until it finds the best way to complete a given task. For example, Microsoft uses this technique in game environments like Minecraft to see how “software agents” improve their work. The system learns through them to modify its behavior based on “rewards” for completing the assigned task, without being specifically programmed to do it in a certain way.
• Supervised learning occurs when machines are trained with labeled data. For example, photos with descriptions of the things that appear in them. The algorithm the machine uses is able to select these labels in other databases. Therefore, if a group of images has been labeled that show dogs, the machine can identify similar images.
• Finally, in the case of unsupervised learning, machines do not identify patterns in labeled databases. Instead, they look for similarities. The algorithms are not programmed to detect a specific type of data, such as images of dogs, but to look for examples that are similar and can be grouped together. This is what occurs, for example, in facial recognition where the algorithm does not look for specific features, but for a series of common patterns that “tell” it that it’s the same face.

Flexibility, adaptation and creativity

Machine learning models, and specifically reinforcement learning, have a characteristic that make them especially useful for the corporate world. “It’s their flexibility and ability to adapt to changes in the data as they occur in the system and learn from the model’s own actions. Therein lies the learning and momentum that was missing from previous techniques,” adds Juan Murillo.

In the case of AlphaGo, this means that the machine adapts based on the opponent’s movements and it uses this new information to constantly improve the model. The latest version of this computer called AlphaGo Zero is capable of accumulating thousands of years of human knowledge after working for just a few days. Furthermore, “AlphaGo Zero also discovered new knowledge, developing unconventional strategies and creative new moves,” explains DeepMind, the Google subsidiary that is responsible for its development, in an article.

INNOVATION AND TECHNOLOGY

Artificial Intelligence, an ally against climate change

The extinction of species, the rise in temperatures and major natural disasters are some of the consequences of climate change. Countries and industries are aware and work to combat the planet’s accelerating pollution. Are there any viable solutions? According to some researches, using big data and machine learning could help drive energy efficiency, transforms industries such as the agriculture and find new eco-friendly construction materials.

This unprecedented ability to adapt has enormous potential to enhance scientific disciplines as diverse as the creation of synthetic proteins or the design of more efficient antennas. “The industrial applications of this technique include continuously optimizing any type of ‘system’,” explains José Antonio Rodríguez, Senior Data Scientist at BBVA’s AI Factory. In the banking world, deep learning also makes it possible to “create algorithms that can adjust to changes in market and customer behavior in order to balance supply and demand, for example, offering personalized prices,” concludes Rodríguez.

Another example is the improvement in systems like those in self-driving cars, which have made great strides in recent years thanks to deep learning. It allows them to progressively enhance their precision; the more they drive, the more data they can analyze. The possibilities of machine learning are virtually infinite as long as data is available they can use to learn. Some researchers are even testing the limits of what we call creativity, using this technology to create art or write articles.

Keep reading about

• Artificial Intelligence
• Big Data
• Innovation

• Up

TRANSFORMATION 03 Feb 2023

BBVA takes its investment banking platform to the Amazon Web Services cloud

BBVA has chosen Amazon Web Services, Inc. (AWS), an Amazon.com, Inc. company (NASDAQ: AMZN), to take the most sophisticated operations of the Corporate and Investment Banking area (BBVA CIB) to the cloud. Specifically, the new platform provides greater computing power to make calculations related to financial markets faster, more accurate and more efficient.

In corporate banking, and especially in financial markets, more complex and precise calculations are required to a greater extent to meet customer demands. At BBVA CIB in particular, this need is evident in the valuations of complex transactions, risk scenarios and regulatory requirements in different business units.

DIGITAL PROCESSING

BBVA collaborates with Amazon Web Services and Bloomberg to develop a new technology to boost its equity business

BBVA, in collaboration with Amazon Web Services (AWS) and Bloomberg, has developed a new cloud-based technology for the equity markets area of its Corporate & Investment Banking unit. The bank has created ‘BBVA C-fit’, a platform built around AWS and Bloomberg technologies, to provide its equity trading team with one of the most cutting edge platforms available in the financial markets sector.

This need is met with specialized technologies known as High Performance Computing (HPC), which allow millions of calculations to be performed at the same time, resulting in more accurate and faster valuation processes for corporate and institutional clients.

BBVA decided to collaborate with AWS for HPC workloads and to help provide the necessary infrastructure resources to improve these computations. BBVA relies on Amazon Elastic Compute Cloud (Amazon EC2) to drive computing and data processing operations. This collaboration also equips traders, data scientists and analysts with the flexibility and elasticity needed to have the cloud technology resources fully adjusted to real-time needs and demand at any given moment. This includes short periods of time to perform valuations of complex operations or risk scenarios, while maximizing turnaround time efficiency. In addition, the use of this new platform and the pay-per-use model will allow BBVA to significantly reduce service costs.

In line with the transformation strategy, this milestone makes it easier for BBVA to continue leveraging cloud capabilities to sustainably increase the efficiency of the service it provides to its corporate customers. According to 451 Research, AWS infrastructure is five times more energy efficient than an average European enterprise data center.

For Enrique Checa, Global Head of Architecture and Infrastructure at BBVA CIB, “The flexibility, scalability and possibilities provided by AWS cloud solutions in this project allow us to take a very important technological leap forward and be ready for the future,”

Yves Dupuy, Head of Global Banking, Southern Europe at AWS, said: “BBVA is an example of a company that works with the customer in mind, aiming to make their experience easier. By employing AWS’ extensive portfolio of cloud services, BBVA can continue to innovate and launch new financial solutions that will help BBVA CIB expand its business and help make it more efficient using AWS’s global infrastructure that can allow them to accelerate processes, reduce costs, scale quickly and increase flexibility.”

In this way, BBVA strengthens its commitment to cloud technology as an essential part of its innovation strategy, while at the same time reinforcing its collaboration with AWS. In addition to the pioneering equity platform, developed jointly with Bloomberg, AWS is one of BBVA’s strategic collaborator with whom it has been cooperating for more than four years to drive digitalization and innovation within the Group.

What is machine learning?
Machine learning (ML) is a type of artificial intelligence (AI) that allows software applications to become more accurate at predicting outcomes without being explicitly programmed to do so. Machine learning algorithms use historical data as input to predict new output values.

Recommendation engines are a common use case for machine learning. Other popular uses include fraud detection, spam filtering, malware threat detection, business process automation (BPA) and Predictive maintenance.

Why is machine learning important?
Machine learning is important because it gives enterprises a view of trends in customer behavior and business operational patterns, as well as supports the development of new products. Many of today’s leading companies, such as Facebook, Google and Uber, make machine learning a central part of their operations. Machine learning has become a significant competitive differentiator for many companies.

What are the different types of machine learning?
Classical machine learning is often categorized by how an algorithm learns to become more accurate in its predictions. There are four basic approaches:supervised learning, unsupervised learning, semi-supervised learning and reinforcement learning. The type of algorithm data scientists choose to use depends on what type of data they want to predict.

Supervised learning: In this type of machine learning, data scientists supply algorithms with labeled training data and define the variables they want the algorithm to assess for correlations. Both the input and the output of the algorithm is specified.
Unsupervised learning: This type of machine learning involves algorithms that train on unlabeled data. The algorithm scans through data sets looking for any meaningful connection. The data that algorithms train on as well as the predictions or recommendations they output are predetermined.
Semi-supervised learning: This approach to machine learning involves a mix of the two preceding types. Data scientists may feed an algorithm mostly labeled training data, but the model is free to explore the data on its own and develop its own understanding of the data set.
Reinforcement learning: Data scientists typically use reinforcement learning to teach a machine to complete a multi-step process for which there are clearly defined rules. Data scientists program an algorithm to complete a task and give it positive or negative cues as it works out how to complete a task. But for the most part, the algorithm decides on its own what steps to take along the way.

How does supervised machine learning work?
Supervised machine learning requires the data scientist to train the algorithm with both labeled inputs and desired outputs. Supervised learning algorithms are good for the following tasks:

Binary classification: Dividing data into two categories.
Multi-class classification: Choosing between more than two types of answers.
Regression modeling: Predicting continuous values.
Ensembling: Combining the predictions of multiple machine learning models to produce an accurate prediction.

How does unsupervised machine learning work?
Unsupervised machine learning algorithms do not require data to be labeled. They sift through unlabeled data to look for patterns that can be used to group data points into subsets. Most types of deep learning, including neural networks, are unsupervised algorithms. Unsupervised learning algorithms are good for the following tasks:

Clustering: Splitting the dataset into groups based on similarity.
Anomaly detection: Identifying unusual data points in a data set.
Association mining: Identifying sets of items in a data set that frequently occur together.
Dimensionality reduction: Reducing the number of variables in a data set.

How does semi-supervised learning work?

Semi-supervised learning works by data scientists feeding a small amount of labeled training data to an algorithm. From this, the algorithm learns the dimensions of the data set, which it can then apply to new, unlabeled data. The performance of algorithms typically improves when they train on labeled data sets. But labeling data can be time consuming and expensive. Semi-supervised learning strikes a middle ground between the performance of supervised learning and the efficiency of unsupervised learning. Some areas where semi-supervised learning is used include:

• Machine translation: Teaching algorithms to translate language based on less than a full dictionary of words.
• Fraud detection: Identifying cases of fraud when you only have a few positive examples.
• Labelling data: Algorithms trained on small data sets can learn to apply data labels to larger sets automatically.

How does reinforcement learning work?

Reinforcement learning works by programming an algorithm with a distinct goal and a prescribed set of rules for accomplishing that goal. Data scientists also program the algorithm to seek positive rewards — which it receives when it performs an action that is beneficial toward the ultimate goal — and avoid punishments — which it receives when it performs an action that gets it farther away from its ultimate goal. Reinforcement learning is often used in areas such as:

• Robotics: Robots can learn to perform tasks the physical world using this technique.
• Video gameplay: Reinforcement learning has been used to teach bots to play a number of video games.
• Resource management: Given finite resources and a defined goal, reinforcement learning can help enterprises plan out how to allocate resources.

Who’s using machine learning and what’s it used for?

Today, machine learning is used in a wide range of applications. Perhaps one of the most well-known examples of machine learning in action is the recommendation engine that powers Facebook’s news feed.

Facebook uses machine learning to personalize how each member’s feed is delivered. If a member frequently stops to read a particular group’s posts, the recommendation engine will start to show more of that group’s activity earlier in the feed.

Behind the scenes, the engine is attempting to reinforce known patterns in the member’s online behavior. Should the member change patterns and fail to read posts from that group in the coming weeks, the news feed will adjust accordingly.

In addition to recommendation engines, other uses for machine learning include the following:

• Customer relationship management. CRM software can use machine learning models to analyze email and prompt sales team members to respond to the most important messages first. More advanced systems can even recommend potentially effective responses.
• Business intelligence. BI and analytics vendors use machine learning in their software to identify potentially important data points, patterns of data points and anomalies.
• Human resource information systems. HRIS systems can use machine learning models to filter through applications and identify the best candidates for an open position.
• Self-driving cars. Machine learning algorithms can even make it possible for a semi-autonomous car to recognize a partially visible object and alert the driver.
• Virtual assistants. Smart assistants typically combine supervised and unsupervised machine learning models to interpret natural speech and supply context.

What are the advantages and disadvantages of machine learning?

Machine learning has seen use cases ranging from predicting customer behavior to forming the operating system for self-driving cars.

When it comes to advantages, machine learning can help enterprises understand their customers at a deeper level. By collecting customer data and correlating it with behaviors over time, machine learning algorithms can learn associations and help teams tailor product development and marketing initiatives to customer demand.

Some companies use machine learning as a primary driver in their business models. Uber, for example, uses algorithms to match drivers with riders. Google uses machine learning to surface the ride advertisements in searches.

But machine learning comes with disadvantages. First and foremost, it can be expensive. Machine learning projects are typically driven by data scientists, who command high salaries. These projects also require software infrastructure that can be expensive.

There is also the problem of machine learning bias. Algorithms trained on data sets that exclude certain populations or contain errors can lead to inaccurate models of the world that, at best, fail and, at worst, are discriminatory. When an enterprise bases core business processes on biased models it can run into regulatory and reputational harm.

How to choose the right machine learning model

The process of choosing the right machine learning model to solve a problem can be time consuming if not approached strategically.

Step 1: Align the problem with potential data inputs that should be considered for the solution. This step requires help from data scientists and experts who have a deep understanding of the problem.

Step 2: Collect data, format it and label the data if necessary. This step is typically led by data scientists, with help from data wranglers.

Step 3: Chose which algorithm(s) to use and test to see how well they perform. This step is usually carried out by data scientists.

Step 4: Continue to fine tune outputs until they reach an acceptable level of accuracy. This step is usually carried out by data scientists with feedback from experts who have a deep understanding of the problem.

Importance of human interpretable machine learning

Explaining how a specific ML model works can be challenging when the model is complex. There are some vertical industries where data scientists have to use simple machine learning models because it’s important for the business to explain how every decision was made. This is especially true in industries with heavy compliance burdens such as banking and insurance.

Complex models can produce accurate predictions, but explaining to a lay person how an output was determined can be difficult. 

What is the future of machine learning?

While machine learning algorithms have been around for decades, they’ve attained new popularity as artificial intelligence has grown in prominence. Deep learning models, in particular, power today’s most advanced AI applications.

Machine learning platforms are among enterprise technology’s most competitive realms, with most major vendors, including Amazon, Google, Microsoft, IBM and others, racing to sign customers up for platform services that cover the spectrum of machine learning activities, including data collection, data preparation, data classification, model building, training and application deployment.

As machine learning continues to increase in importance to business operations and AI becomes more practical in enterprise settings, the machine learning platform wars will only intensify.

Continued research into deep learning and AI is increasingly focused on developing more general applications. Today’s AI models require extensive training in order to produce an algorithm that is highly optimized to perform one task. But some researchers are exploring ways to make models more flexible and are seeking techniques that allow a machine to apply context learned from one task to future, different tasks.

How has machine learning evolved?

1642 – Blaise Pascal invents a mechanical machine that can add, subtract, multiply and divide.

1679 – Gottfried Wilhelm Leibniz devises the system of binary code.

1834 – Charles Babbage conceives the idea for a general all-purpose device that could be programmed with punched cards.

1842 – Ada Lovelace describes a sequence of operations for solving mathematical problems using Charles Babbage’s theoretical punch-card machine and becomes the first programmer.

1847 – George Boole creates Boolean logic, a form of algebra in which all values can be reduced to the binary values of true or false.

1936 – English logician and cryptanalyst Alan Turing proposes a universal machine that could decipher and execute a set of instructions. His published proof is considered the basis of computer science.

1952 – Arthur Samuel creates a program to help an IBM computer get better at checkers the more it plays.

1959 – MADALINE becomes the first artificial neural network applied to a real-world problem: removing echoes from phone lines.

1985 – Terry Sejnowski’s and Charles Rosenberg’s artificial neural network taught itself how to correctly pronounce 20,000 words in one week.

1997 – IBM’s Deep Blue beat chess grandmaster Garry Kasparov.

1999 – A CAD prototype intelligent workstation reviewed 22,000 mammograms and detected cancer 52% more accurately than radiologists did.

2006 – Computer scientist Geoffrey Hinton invents the term deep learning to describe neural net research.

2012 – An unsupervised neural network created by Google learned to recognize cats in YouTube videos with 74.8% accuracy.

2014 – A chatbot passes the Turing Test by convincing 33% of human judges that it was a Ukrainian teen named Eugene Goostman.

2014 – Google’s AlphaGo defeats the human champion in Go, the most difficult board game in the world.

2016 – LipNet, DeepMind’s artificial intelligence system, identifies lip-read words in video with an accuracy of 93.4%.

2019 – Amazon controls 70% of the market share for virtual assistants in the U.S.

This was last updated in March 2021