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.
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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.
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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.
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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!