What Is Machine Learning and How Does It Work?

Machine learning is the science of enabling computers to function without being programmed to do so.

This branch of artificial intelligence can enable systems to identify patterns in data, make decisions, and predict future outcomes. Machine learning can help companies determine the products you’re most likely to buy and even the online content you’re most likely to consume and enjoy.

Machine learning makes it easier to analyze and interpret massive amounts of data, which would otherwise take decades or even an eternity for humans to decode. 

In effect, machine learning is an attempt to teach computers to think, learn, and act like humans. Thanks to increasing internet speeds, advancements in storage technology, and expanding computational power, machine learning has exponentially advanced and become an integral part of almost every industry.

What is machine learning?

Machine learning (ML) is a branch of artificial intelligence (AI) that focuses on building applications that can automatically and periodically learn and improve from experience without being explicitly programmed.

With the backing of machine learning, applications become more accurate at decision-making and predicting outcomes. As a matter of fact, machine learning is responsible for the majority of advancements in the field of artificial intelligence and is an integral part of data science.

By granting computers the ability to learn and improve, they can solve real-world problems without being specifically instructed to do so. For that, machine learning algorithms are trained to perform pattern recognition in vast amounts of data or big data.

Recommendation systems are one of the most common applications of machine learning. Companies like Google, Netflix, and Amazon use machine learning to understand preferences better and use the information to recommend products and services.

The emphasis here is on leveraging data. By implementing statistics on vast volumes of data, machine learning algorithms can find patterns and use these patterns to make predictions. In other words, these algorithms can utilize historical data as input and predict new output values.

Collecting data is easy. But analyzing and making sense of vast volumes of data is the hardest part. That’s where machine learning makes all the difference. If a specific dataset can be digitally stored, it can be fed into an ML algorithm and processed to gain valuable insights.

Machine learning vs. traditional programming

Traditional software applications have a narrower scope. They depend on explicit instructions from humans to work and can’t think for themselves. These specific instructions could be something like ‘if you see X, then perform Y’.

Machine learning, on the other hand, doesn’t require any explicit instruction to function. Instead, you give an application the essential data and tools needed to study a problem and it will solve it without being told what to do. Additionally, you also provide the application the ability to remember what it did so that it can learn, adapt, and improve periodically – similar to humans.

If you’re going with traditional programming and by the ‘if X then Y’ route, then things can get messy. 

Suppose you create a spam detection application that deletes all spammy emails. To identify such emails, you explicitly instruct the application to look for terms like “earn,” “free,” and “zero investment”.

A spammer can easily manipulate the system by choosing synonyms of these terms or replacing certain characters with numbers. The spam detection application will also come across numerous false positives, such as when your friend sends a genuine email containing a code for free movie tickets.

Such limitations can be eliminated by machine learning. Instead of inputting instructions, machine learning requires data to learn and understand what a malicious email would look like. By learning by example (not instructions), the application gets better with time and can detect and delete spam messages more accurately.

Still not convinced why machine learning is a godsend technology?

Here are some situations where machine learning becomes invaluable:

  • If the rules of a particular task continually change, for example, in the case of fraud detection, traditional applications will break, but machine learning can handle the variations.
  • In the case of image recognition, the rules are too complex to be hand-written. Also, it’s virtually impossible for a human to code every distinction and feature into the application. A machine learning algorithm can learn to identify these features by analyzing huge volumes of image data.
  • A traditional application will falter if the nature of the data it processes changes. In the case of demand forecasting or predicting upcoming trends, the type of data might frequently change, and a machine learning application can adapt with ease.

Want to learn more about Machine Learning Software? Explore Machine Learning products.

A brief history of machine learning

Machine learning has been around for quite some time. It’s easy to tell that because computers are rarely referred to as “machines” anymore. Here’s a quick look at the evolution of machine learning from inception to realization.

Pre-1920s: Thomas Bayes, Andrey Markov, Adrien-Marie Legendre, and other acclaimed mathematicians lay the necessary groundwork for the foundational machine learning techniques.

1943: The first mathematical model of neural networks is presented in a scientific paper by Walter Pitts and Warren McCulloch.

1949: The Organization of Behavior, a book by Donald Hebb, is published. This book explores how behavior relates to brain activity and neural networks.

1950: Alan Turing tries to describe artificial intelligence and questions whether machines have the capabilities to learn.

1951: Marvin Minsky and Dean Edmonds built the very first artificial neural network.

1956: John McCarthy, Marvin Minsky, Nathaniel Rochester, and Claude Shannon organized the Dartmouth Workshop. The event is often referred to as the “birthplace of AI,” and the term “artificial intelligence” was coined in the same event.

1965: Alexey (Oleksii) Ivakhnenko and Valentin Lapa developed the first multi-layer perceptron. Ivakhnenko is often regarded as the father of deep learning (a subset of machine learning).

1967: The nearest neighbor algorithm is conceived.

1979: Computer scientist Kunihiko Fukushima published his work on neocognitron: a hierarchical multilayered network used to detect patterns. Neocognitron also inspired convolutional neural networks (CNNs).

1985: Terrence Sejnowski invents NETtalk. This program learns to pronounce (English) words the same way babies do.

1995: Tin Kam Ho introduces random decision forests in a paper.

1997: Deep Blue, the IBM chess computer, beats Garry Kasparov, the world champion in chess.

2000: The term “deep learning” was first mentioned by neural networks researcher Igor Aizenberg.

2009: ImageNet, a large image database extensively used for visual object recognition research, is launched by Fei-Fei Li.

2011: Google’s X Lab developed Google Brain, an artificial intelligence algorithm. Later this year, IBM Watson beat human competitors on the trivia game show Jeopardy!.

2014: Ian Goodfellow and his colleagues develop a generative adversarial network (GAN). The same year, Facebook developed DeepFace. It’s a deep learning facial recognition system that can spot human faces in images with nearly 97.25% accuracy. Later, Google introduces a large-scale machine learning system called Sibyl to the public.

2015: AlphaGo becomes the first AI to beat a professional player at Go.

2020: Open AI announces GPT-3, a robust natural language processing algorithm with the ability to generate human-like text.

How does machine learning work?

At its heart, machine learning algorithms analyze and identify patterns from datasets and use this information to make better predictions on new data sets.

It’s similar to how humans learn and improve. Whenever we make a decision, we consider our past experiences to assess the situation better. A machine learning model does the same by analyzing historical data to make predictions or decisions. After all, machine learning is an AI application that enables machines to self-learn from data.

To get a simple understanding of how machine learning works, imagine how you would learn to play the dinosaur game – a game you would’ve come across only if you use Google Chrome and have an unreliable internet connection.

The game will end only after 17 million years of playtime (the approximate number of years the game character, the T-Rex dinosaur, existed before they went extinct). So finishing the game is out of the question.

In case you haven’t played the game before, you have to jump whenever the T-Rex encounters a cactus plant and jump or duck whenever it encounters a bird.

As a human, you would use the trial and error method to learn how to play the game. By playing the game a couple of times, you could easily understand that to not lose, you need to avoid running into the cactus or the bird.

An AI application would also learn almost similarly. A developer could specify in the application’s code to jump 1/20th of the time whenever it encounters a dense area of dark pixels. If the particular action reduced the chances of losing, it could be increased to jump 1/10th of the time. By playing more and encountering more obstacles, the application could predict when to jump or duck.

More precisely, the application would continually collect data regarding actions, environment, and outcomes. The collected data is usually used to develop a graph. After many trials and errors, the AI could plot a graph that could help predict the most suitable action: jump or duck.

Here’s another example. 

Consider the following sequence.

  • 3 – 9
  • 4 – 16
  • 5 – 25

So if you were given the number 6, which number would you pick so that the pair would match the above sequence?

If you concluded that it’s 36, how did you do it?

You probably analyzed the previous data (historical data) and “predicted” the number with the highest probability. A machine learning model is no different. It learns from experience and uses the accumulated information to make better predictions.

In essence, machine learning is pure math. Any and every machine learning algorithm is built around a mathematical function that can be modified. This also means that the learning process in machine learning is also based on mathematics.

4 types of machine learning methods

There are numerous machine learning methods by which AI systems can learn from data. These methods are categorized based on the nature of data (labeled or unlabeled) and the results you anticipate. Generally, there are four types of machine learning: supervised, unsupervised, semi-supervised, and reinforcement learning.

1. Supervised learning

Supervised learning is a machine learning approach in which a data scientist acts like a tutor and trains the AI system by feeding basic rules and labeled datasets. The datasets will include labeled input data and expected output results. In this machine learning method, the system is explicitly told what to look for in the input data.

In simpler terms, supervised learning algorithms learn by example. Such examples are collectively referred to as training data. Once a machine learning model is trained using the training dataset, it’s given the test data to determine the model’s accuracy.

Supervised learning can be further classified into two types: classification and regression.

2. Unsupervised learning

Unsupervised learning is a machine learning technique in which the data scientist lets the AI system learn by observing. The training dataset will contain only the input data and no corresponding output data.

When compared to supervised learning, this machine learning method requires massive amounts of unlabeled data to observe, find patterns, and learn. Unsupervised learning could be a goal in itself, for example, discovering hidden patterns in datasets or a method for feature learning.

Unsupervised learning problems are generally grouped into clustering and association problems.

3. Semi-supervised learning

Semi-supervised learning is an amalgam of supervised and unsupervised learning. In this machine learning process, the data scientist trains the system just a little bit so that it gets a high-level overview.

Also, a small percentage of the training data will be labeled, and the remaining will be unlabeled. Unlike supervised learning, this learning method demands the system to learn the rules and strategy by observing patterns in the dataset.

Semi-supervised learning is beneficial when you don’t have enough labeled data, or the labeling process is expensive, but you want to create an accurate machine learning model.

4. Reinforcement learning

Reinforcement learning (RL) is a learning technique that allows an AI system to learn in an interactive environment. A programmer will use a reward-penalty approach to teach the system, enabling it to learn by trial and error and receiving feedback from its own actions.

Simply put, in reinforcement learning, the AI system will face a game-like situation in which it has to maximize the reward.

Although the programmer defines the game rules, the individual doesn’t provide any hints on how to solve or win the game. The system must find its way by making numerous random trials and learn to improve from each step.

Uses of machine learning

It’s safe to say that machine learning has impacted almost every field that underwent a digital transformation. This branch of artificial intelligence has immense potential when it comes to task automation, and its predictive capabilities are saving lives in the healthcare industry.

Here are some of the many use cases of machine learning.

Image recognition

Machines are getting better at processing images. In fact, machine learning models are better and faster in recognizing and classifying images than humans.

This application of machine learning is called image recognition or computer vision. It’s powered by deep learning algorithms and uses images as the input data. You have most likely seen this feat in action when you uploaded a photo on Facebook and the app suggested tagging your friends by recognizing their faces.

Customer relationship management (CRM) software

Machine learning enables CRM software applications to decode the “why” questions. 

Why does a specific product outperform the rest? Why do customers make a particular action on the website? Why aren’t customers satisfied with a product?

By analyzing historical data collected by CRM applications, machine learning models can help build better sales strategies and even predict emerging market trends. ML can also find means to reduce churn rates, improve customer lifetime value, and help companies stay one step ahead.

Along with data analysis, marketing automation, and predictive analytics, machine learning grants companies the ability to be available 24/7 by its embodiment as chatbots.

Patient diagnosis

It’s safe to say that paper medical records are a thing of the past. A good number of hospitals and clinics have now adopted electronic health records (EHRs), making the storage of patient information more secure and efficient.

Since EHRs convert patient information to a digital format, the healthcare industry gets to implement machine learning and eradicate tedious processes. This also means that doctors can analyze patient data in real time and even predict the possibilities of disease outbreaks.

Along with enhancing medical diagnosis accuracy, machine learning algorithms can help doctors detect breast cancer and predict a disease’s progression rate.

Inventory optimization

If a specific material is stored in excess, it may not be used before it gets spoiled. On the other hand, if there’s a shortage, the supply chain will be affected. The key is to maintain inventory by considering the product demand.

The demand for a product can be predicted based on historical data. For example, ice cream is sold more frequently during the summer season (although not always and everywhere). However, numerous other factors affect the demand, including the day of the week, temperature, upcoming holidays, and more.

Computing such micro and macro factors is virtually impossible for humans. Not surprisingly, processing such massive volumes of data is a specialty of machine learning applications.

For instance, by leveraging The Weather Company’s enormous database, IBM Watson found that yogurt sales increase when the wind is above average, and autogas sales spike when the temperature is colder than average.

Additionally, self-driving cars, demand forecasting, speech recognition, recommendation systems, and anomaly detection wouldn’t have been possible without machine learning.

How to build a machine learning model?

Creating a machine learning model is just like developing a product. There’s ideation, validation, and testing phase, to name a few processes. Generally, building a machine learning model can be broken down into five steps.

Collecting and preparing training dataset

In the machine learning realm, nothing is more important than quality training data.

As mentioned earlier, the training dataset is a collection of data points. These data points help the model to understand how to tackle the problem it’s intended to solve. Typically, the training dataset contains images, text, video, or audio.

The training dataset is similar to a math textbook with example problems. The greater the number of examples, the better. Along with quantity, the dataset’s quality also matters as the model needs to be highly accurate. The training dataset must also reflect the real-world conditions in which the model will be used.

The training dataset can be fully labeled, unlabeled, or partially labeled. As mentioned earlier, this nature of the dataset is dependent on the machine learning method you choose.

Either way, the training dataset must be devoid of duplicate data. A high-quality dataset will undergo numerous stages of the cleaning process and contain all the essential attributes you want the model to learn.

Always keep this phrase in mind: garbage in, garbage out.

Choose an algorithm

An algorithm is a procedure or a method to solve a problem. In machine learning language, an algorithm is a procedure run on data to create a machine learning model. Linear regression, logistic regression, k-nearest neighbors (KNN), and Naive Bayes are a few of the popular machine learning algorithms.

Choosing an algorithm depends on the problem you intend to solve, the type of data (labeled or unlabeled), and the amount of data available.

If you’re using labeled data, you can consider the following algorithms:

  • Decision trees
  • Linear regression
  • Logistic regression
  • Support vector machine (SVM)
  • Random forest

If you’re using unlabeled data, you can consider the following algorithms:

  • K-means clustering algorithm
  • Apriori algorithm
  • Singular value decomposition
  • Neural networks

Also, if you want to train the model to make predictions, choose supervised learning. If you wish to train the model to find patterns or split data into clusters, go for unsupervised learning.

Train the algorithm

The algorithm goes through numerous iterations in this phase. After each iteration, the weights and biases within the algorithm are adjusted by comparing the output with the expected results. The process continues until the algorithm becomes accurate, which is the machine learning model.

Validate the model

For many, the validation dataset is synonymous with the test dataset. In short, it’s a dataset not used during the training phase and is introduced to the model for the first time. The validation dataset is critical for assessing the model’s accuracy and understanding whether it suffers from overfitting: an incorrect optimization of a model when it gets overly tuned to its training dataset.

If the model’s accuracy is less than or equal to 50%, it’s unlikely that it would be useful for real-world applications. Ideally, the model must have an accuracy of 90% or more.

Test the model

Once the model is trained and validated, it needs to be tested using real-world data to verify its accuracy. This step might make the data scientist sweat as the model will be tested on a larger dataset, unlike in the training or validation phase.

In a simpler sense, the testing phase lets you check how well the model has learned to perform the specific task. It’s also the phase where you can determine whether the model will work on a larger dataset.

The model gets better over time and with access to newer datasets. For example, your email inbox’s spam filter gets periodically better when you report particular messages as spam and false positives as not spam.

Top 5 machine learning tools

As mentioned earlier, machine learning algorithms are capable of making predictions or decisions based on data. These algorithms grant applications the ability to offer automation and AI features. Interestingly, the majority of end-users aren’t aware of the usage of machine learning algorithms in such intelligent applications.

To qualify for inclusion in the machine learning category, a product must:

  • Offer a product or algorithm capable of learning and improving by leveraging data
  • Be the source of intelligent learning abilities in software applications
  • Be capable of utilizing data inputs from different data pools
  • Have the ability to produce an output that solves a particular issue based on the learned data

Below are the five leading machine learning software from G2’s Winter 2021 Grid® Report. Some reviews may be edited for clarity.

1. scikit-learn

scikit-learn is a machine learning library for the Python programming language that offers several supervised and unsupervised machine learning algorithms. It contains various statistical modeling and machine learning tools such as classification, regression, and clustering algorithms.

The library is designed to interoperate with the Python numerical and scientific libraries like NumPy and SciPy. scikit-learn can also be used for extracting features from text and images.

What users like:

“The best aspect of this framework is the availability of well-integrated algorithms within the Python development environment. It’s quite easy to install within most Python IDEs and relatively easy to use. Many tutorials are accessible online, making it easier to understand this library. It was clearly built with a software engineering mindset, and nevertheless, it’s very flexible for research ventures. Being built on top of multiple math-based and data libraries, scikit-learn allows seamless integration between them all.

Being able to use NumPy arrays and Pandas DataFrames within the scikit-learn environment removes the need for additional data transformation. That being said, one should definitely get familiar with this easy-to-use library if they plan on becoming a data-driven professional. You can build a simple machine learning model with just ten lines of code! With tons of features like model validation, data splitting for training/testing, and various others, scikit-learn’s open-source approach facilitates a manageable learning curve.”

– scikit-learn Review, Devwrat T.

What users dislike:

“It has great features. However, it has some drawbacks in dealing with categorical attributes. Otherwise, it’s a robust package. I don’t see any other drawbacks to using this package.”

– scikit-learn Review, User in Higher Education

2. Personalizer

Personalizer is a cloud-based service from Microsoft used to deliver personalized, relevant experiences to users. With the help of reinforcement learning, this easy-to-use API helps in improving digital store conversions.

After delivering content, the tool monitors users’ reactions, thereby learning in real time and making the best use of contextual information. Personalizer can be embedded into an app by adding just two lines of code, and it can start with no data.

What users like:

“The ease of us is absolutely wonderful. We got the configuration and our products recommended on our site in no time. After deployment, the app integration was so great that sometimes we forget it’s running in the background doing all the heavy work.”

– Personalizer Review, G2 User in Information Technology and Services

What users dislike:

“There is some lack of documentation online, but it isn’t really needed for the configuration.”

– Personalizer Review, G2 User in Financial Services

3. Google Cloud TPU

Google Cloud TPU is a custom-designed machine learning application-specific integrated circuit (ASIC) designed to run machine learning models with AI services on Google cloud. It offers more than 100 petaflops of performance in just a single pod, which is enough computational power for business and research needs.

What users like:

“I love the fact that we were able to build a state-of-the-art AI service geared towards network security thanks to the optimal running of the cutting-edge machine learning models. The power of Google Cloud TPU is of no match: up to 11.5 petaflops and 4 TB HBM. Best of all, the straight-forward easy to use Google Cloud Platform interface.”

– Google Cloud TPU Review, Isabelle F.

What users dislike:

“I wish there were integration with word processors.”

– Google Cloud TPU Review, Kevin C.

4. Amazon Personalize

Amazon Personalize is a machine learning service that enables developers to build applications with real-time personalized recommendations without any ML expertise. This ML service offers the necessary infrastructure and can be implemented in days. The service also manages the entire ML pipeline, including data processing and identifying the features, as well as training, optimizing, and hosting the models.

What users like:

“Amazon as a whole is usually two steps ahead. But Amazon Personalize takes it to a whole new level. It’s easy to use, perfect for small companies/entrepreneurs, and unique.”

– Amazon Personalize Review, Melissa B.

What users dislike:

“At this point, the only issue is that we have to filter through too many options so that our consumers are not constantly receiving repetitive recommendations.”

– Amazon Personalize Review, G2 User in Higher Education

5. machine-learning in Python

machine-learning in Python is a project that offers a web-interface and a programmatic-API for support vector machine (SVM) and support vector regression (SVR) machine learning algorithms.

What users like:

“Python is an easy to use machine learning programming language which has extensive libraries and packages. Its packages provide efficient visualization to understand. Also, nowadays, it’s used for purposes like automated scripting in cybersecurity.”

– machine-learning in Python Review, Manisha S.

What users dislike:

“Documentation for some functions is rather limited. Not every implemented algorithm is present. Most of the additional libraries are easy to install, but some can be quite cumbersome and take a while.”

– machine-learning in Python Review, G2 User in Higher Education

How machines learn the human world

Along with recommending the products and services you’re more likely to enjoy, machine learning algorithms act as a watchful protector that ensures you aren’t cheated by online fraudsters and keeps your email inbox clean of spam messages. In short, it’s a learning process that helps machines get to know the human world around them.

If machine learning hit the gym five days a week, we would get deep learning. It’s a subset of machine learning that mimics the functioning of the human brain. Read more about deep learning and why it’s crucial for creating robots with human-like intelligence.

Machine learning: What is it and how does it work?

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.


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.


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


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.

BBVA lleva a la nube de Amazon Web Services su plataforma de banca de inversión

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.


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 and How Does It Work?

Table of Contents

What is Machine Learning, Exactly?

How Does Machine Learning Work?

What are the Different Types of Machine Learning?

Why is Machine Learning Important?

Main Uses of Machine Learning

How Do You Decide Which Machine Learning Algorithm to Use?

What is the Best Programming Language for Machine Learning?

Enterprise Machine Learning and MLOps

A Look at Some Machine Learning Algorithms and Processes

Prerequisites for Machine Learning (ML)

So, What Next?

Machine learning is an exciting branch of Artificial Intelligence, and it’s all around us. Machine learning brings out the power of data in new ways, such as Facebook suggesting articles in your feed. This amazing technology helps computer systems learn and improve from experience by developing computer programs that can automatically access data and perform tasks via predictions and detections.

As you input more data into a machine, this helps the algorithms teach the computer, thus improving the delivered results. When you ask Alexa to play your favorite music station on Amazon Echo, she will go to the station you played most often. You can further improve and refine your listening experience by telling Alexa to skip songs, adjust the volume, and many more possible commands. Machine Learning and the rapid advance of Artificial Intelligence makes this all possible.

Let us start by answering the question – What is Machine Learning?

What is Machine Learning, Exactly?

For starters, machine learning is a core sub-area of Artificial Intelligence (AI). ML applications learn from experience (or to be accurate, data) like humans do without direct programming. When exposed to new data, these applications learn, grow, change, and develop by themselves. In other words, machine learning involves computers finding insightful information without being told where to look. Instead, they do this by leveraging algorithms that learn from data in an iterative process.

The concept of machine learning has been around for a long time (think of the World War II Enigma Machine, for example). However, the idea of automating the application of complex mathematical calculations to big data has only been around for several years, though it’s now gaining more momentum.

At a high level, machine learning is the ability to adapt to new data independently and through iterations.  Applications learn from previous computations and transactions and use “pattern recognition” to produce reliable and informed results.

Now that we understand what Machine Learning is, let us understand how it works and why you should opt for an AI course like our AI & Machine Learning Bootcamp today!

How Does Machine Learning Work?

Machine Learning is, undoubtedly, one of the most exciting subsets of Artificial Intelligence. It completes the task of learning from data with specific inputs to the machine. It’s important to understand what makes Machine Learning work and, thus, how it can be used in the future. 

The Machine Learning process starts with inputting training data into the selected algorithm. Training data being known or unknown data to develop the final Machine Learning algorithm. The type of training data input does impact the algorithm, and that concept will be covered further momentarily. 

New input data is fed into the machine learning algorithm to test whether the algorithm works correctly. The prediction and results are then checked against each other.

If the prediction and results don’t match, the algorithm is re-trained multiple times until the data scientist gets the desired outcome. This enables the machine learning algorithm to continually learn on its own and produce the optimal answer, gradually increasing in accuracy over time.

The next section discusses the three types of and use of machine learning.

Become a Data Scientist by learning from the best with Simplilearn’s Caltech Post Graduate Program In Data Science. Enroll Now!

What are the Different Types of Machine Learning?

Machine Learning is complex, which is why it has been divided into two primary areas, supervised learning and unsupervised learning. Each one has a specific purpose and action, yielding results and utilizing various forms of data. Approximately 70 percent of machine learning is supervised learning, while unsupervised learning accounts for anywhere from 10 to 20 percent. The remainder is taken up by reinforcement learning.

1. Supervised Learning

In supervised learning, we use known or labeled data for the training data. Since the data is known, the learning is, therefore, supervised, i.e., directed into successful execution. The input data goes through the Machine Learning algorithm and is used to train the model. Once the model is trained based on the known data, you can use unknown data into the model and get a new response.

Supervised Learning

In this case, the model tries to figure out whether the data is an apple or another fruit. Once the model has been trained well, it will identify that the data is an apple and give the desired response.

Here is the list of top algorithms currently being used for supervised learning are:

  • Polynomial regression
  • Random forest
  • Linear regression
  • Logistic regression
  • Decision trees
  • K-nearest neighbors
  • Naive Bayes

Now let’s learn about unsupervised learning

The following part of the What is Machine Learning article focuses on unsupervised learning.

2. Unsupervised Learning

In unsupervised learning, the training data is unknown and unlabeled – meaning that no one has looked at the data before. Without the aspect of known data, the input cannot be guided to the algorithm, which is where the unsupervised term originates from. This data is fed to the Machine Learning algorithm and is used to train the model. The trained model tries to search for a pattern and give the desired response. In this case, it is often like the algorithm is trying to break code like the Enigma machine but without the human mind directly involved but rather a machine.

Unsupervised Learning

In this case, the unknown data consists of apples and pears which look similar to each other. The trained model tries to put them all together so that you get the same things in similar groups.

The top 7 algorithms currently being used for unsupervised learning are:

  • Partial least squares
  • Fuzzy means
  • Singular value decomposition
  • K-means clustering
  • Apriori
  • Hierarchical clustering
  • Principal component analysis

3. Reinforcement Learning

Like traditional types of data analysis, here, the algorithm discovers data through a process of trial and error and then decides what action results in higher rewards. Three major components make up reinforcement learning: the agent, the environment, and the actions. The agent is the learner or decision-maker, the environment includes everything that the agent interacts with, and the actions are what the agent does.

Reinforcement learning happens when the agent chooses actions that maximize the expected reward over a given time. This is easiest to achieve when the agent is working within a sound policy framework.

Now let’s see why Machine Learning is such a vital concept today.

Why is Machine Learning Important?

To better answer the question :what is machine learning” and understand the uses of Machine Learning, consider some of the applications of Machine Learning: the self-driving Google car, cyber fraud detection, and online recommendation engines from Facebook, Netflix, and Amazon. Machines make all these things possible by filtering useful pieces of information and piecing them together based on patterns to get accurate results.

The process flow depicted here represents how Machine Learning works:

Machine Learning Process

The rapid evolution in Machine Learning (ML) has caused a subsequent rise in the use cases, demands, and the sheer importance of ML in modern life. Big Data has also become a well-used buzzword in the last few years.  This is, in part, due to the increased sophistication of Machine Learning, which enables the analysis of large chunks of Big Data. Machine Learning has also changed the way data extraction and interpretation are done by automating generic methods/algorithms, thereby replacing traditional statistical techniques.

Now that you know what machine learning is, its types, and its importance, let us move on to the uses of machine learning.

Main Uses of Machine Learning

Typical results from machine learning applications usually include web search results, real-time ads on web pages and mobile devices, email spam filtering, network intrusion detection, and pattern and image recognition. All these are the by-products of using machine learning to analyze massive volumes of data.

Traditionally, data analysis was trial and error-based, an approach that became increasingly impractical thanks to the rise of large, heterogeneous data sets. Machine learning provides smart alternatives for large-scale data analysis. Machine learning can produce accurate results and analysis by developing fast and efficient algorithms and data-driven models for real-time data processing.

Pro Tip: For more on Big Data and how it’s revolutionizing industries globally, check out our “What is Big Data?” article.

According to Marketwatch, the global machine learning market is expected to grow at a healthy rate of over 45.9 percent during the period of 2017-2025. If this trend holds, then we will see a greater use of machine learning across a wide spectrum of industries worldwide. Machine learning is here to stay!

How Do You Decide Which Machine Learning Algorithm to Use?

There are dozens of different algorithms to choose from, but there’s no best choice or one that suits every situation. In many cases, you must resort to trial and error. But there are some questions you can ask that can help narrow down your choices.

  • What’s the size of the data you will be working with?
  • What’s the type of data you will be working with?
  • What kinds of insights are you looking for from the data?
  • How will those insights be used?

What is the Best Programming Language for Machine Learning?

If you’re looking at the choices based on sheer popularity, then Python gets the nod, thanks to the many libraries available as well as the widespread support. Python is ideal for data analysis and data mining and supports many algorithms (for classification, clustering, regression, and dimensionality reduction), and machine learning models.

Enterprise Machine Learning and MLOps

Enterprise machine learning gives businesses important insights into customer loyalty and behavior, as well as the competitive business environment. Machine learning also can be used to forecast sales or real-time demand.

Machine learning operations (MLOps) is the discipline of Artificial Intelligence model delivery. It helps organizations scale production capacity to produce faster results, thereby generating vital business value.

A Look at Some Machine Learning Algorithms and Processes

If you’re studying what is Machine Learning, you should familiarize yourself with standard Machine Learning algorithms and processes. These include neural networks, decision trees, random forests, associations, and sequence discovery, gradient boosting and bagging, support vector machines, self-organizing maps, k-means clustering, Bayesian networks, Gaussian mixture models, and more.

There are other machine learning tools and processes that leverage various algorithms to get the most value out of big data. These include:

  • Comprehensive data quality and management
  • GUIs for building models and process flows
  • Interactive data exploration and visualization of model results
  • Comparisons of different Machine Learning models to quickly identify the best one
  • Automated ensemble model evaluation to determine the best performers
  • Easy model deployment so you can get repeatable, reliable results quickly
  • An integrated end-to-end platform for the automation of the data-to-decision process

Prerequisites for Machine Learning (ML)

For those interested in learning beyond what is Machine Learning, a few requirements should be met to be successful in pursual of this field. These requirements include:

  1. Basic knowledge of programming languages such as Python, R, Java, JavaScript, etc
  2. Intermediate knowledge of statistics and probability
  3. Basic knowledge of linear algebra. In the linear regression model, a line is drawn through all the data points, and that line is used to compute new values.
  4. Understanding of calculus
  5. Knowledge of how to clean and structure raw data to the desired format to reduce the time taken for decision-making.

These prerequisites will improve your chances of successfully pursuing a machine learning career. For a refresh on the above-mentioned prerequisites, the Simplilearn YouTube channel provides succinct and detailed overviews.

Acelerate your career in AI and ML with the AI and ML Courses with Purdue University collaborated with IBM.

So, What Next?

Wondering how to get ahead after this “What is Machine Learning” tutorial? Consider taking Simplilearn’s Artificial Intelligence Course which will set you on the path to success in this exciting field. Master Machine Learning concepts, machine learning steps and techniques including supervised and unsupervised learning, mathematical and heuristic aspects, and hands-on modeling to develop algorithms and prepare you for the role of Machine Learning Engineer.

You can also take the AI and ML Course in partnership with Purdue University. This program gives you in-depth and practical knowledge on the use of machine learning in real world cases. Further, you will learn the basics you need to succeed in a machine learning career like statistics, Python, and data science.

You should also consider accelerating your AI or ML career with the AI Course with Caltech University and in collaboration with IBM.

Machine learning is the future, and the future is now. Are you ready to transform? Start your journey with Simplilearn!