What Is Machine Learning?

Machine learning (ML) is the study of computer algorithms that improve automatically through experience. Many a time, people misconstrue ML as Artificial Intelligence (AI). AI is a broader term which also includes Deep Learning. So think of ML as a subset of AI.

Machine learning algorithms build a mathematical model based on sample data, known as “training data”, in order to make predictions or decisions without being explicitly programmed to do so.

Tom M. Mitchell provided formal definition of ML as:

“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.”

This definition is convoluted to understand without an example. To make things simpler consider a situation in which the computer is supposed to predict weather on a certain day. In this case, the task T is to predict the weather, the experience E is the training samples i.e. data provided to the machine learning algorithm to learn from and the performance measure P is how accurately the algorithm has predicted the weather for that particular day.

You might have guessed by now that ML is classified further into different types. Yes! there are different types of Machine Learning algorithms and each algorithm is designed and suitable for a specific task. There are three major classes:

1. Supervised Learning: Supervised learning as the name indicates the presence of a supervisor as a teacher. Basically supervised learning is a learning in which we teach or train the machine using data which is well labeled that means some data is already tagged with the correct answer. After that, the machine is provided with a new set of examples(data) so that supervised learning algorithm analyses the training data(set of training examples) and produces a correct outcome from labeled data.
2. Unsupervised Learning: Unsupervised learning is the training of machine using information that is neither classified nor labeled and allowing the algorithm to act on that information without guidance. Here the task of machine is to group unsorted information according to similarities, patterns and differences without any prior training of data. Unlike supervised learning, no teacher is provided that means no training will be given to the machine. Therefore machine is restricted to finding the hidden structure in unlabeled data by itself.
3. Reinforcement Learning: Reinforcement learning is the training of machine learning models to make a sequence of decisions. The agent learns to achieve a goal in an uncertain, potentially complex environment. In reinforcement learning, an artificial intelligence faces a game-like situation. The computer employs trial and error to come up with a solution to the problem. To get the machine to do what the programmer wants, the artificial intelligence gets either rewards or penalties for the actions it performs. Its goal is to maximize the total reward. Although the designer sets the reward policy–that is, the rules of the game–he gives the model no hints or suggestions for how to solve the game. It’s up to the model to figure out how to perform the task to maximize the reward, starting from totally random trials and finishing with sophisticated tactics and superhuman skills. By leveraging the power of search and many trials, reinforcement learning is currently the most effective way to hint machine’s creativity.

There are many algorithms within each aforementioned category like Regression and Classification algorithms. We will further discuss each algorithm in detail along with their implementation.