Chapter 2. Let's Help Machines Learn

Machine learning, when you first hear it, sounds more like a fancy word from a sci-fi movie than the latest trend in the tech industry. Talk about it to people in general and their responses are either related to being generally curious about the concept or being cautious and fearful about intelligent machines taking over our world in some sort of Terminator-Skynet way.

We live in a digital age and are constantly presented with all sorts of information all the time. As we will see in this and the coming chapters, machine learning is something that loves data. In fact, the recent hype and interest in this field has been fueled by not just the improvements in computing technology but also due to exponential growth in the amount of data being generated every second. The latest numbers stand at around 2.5 quintillion bytes of data every day (that's 2.5 followed by 18 zeroes)!

Note

Fun Fact: More than 300 hours of video data is uploaded to YouTube every minute...

Understanding machine learning

Aren't we taught that computer systems have to be programmed to do certain tasks? They may be a million times faster at doing things but they have to be programmed. We have to code each and every step and only then do these systems work and complete a task. Isn't then the very notion of machine learning a very contradictory concept?

In the simplest ways, machine learning refers to a method of teaching the systems to learn to do certain tasks, such as learning a function. As simple as it sounds, it is a bit confusing and difficult to digest. Confusing because our view of the way the systems (computer systems specifically) work and the way we learn are two concepts that hardly intersect. It is even more difficult to digest because learning, though an inherent capability of the human race, is difficult to put in to words, let alone teach to the systems.

Then what is machine learning? Before we even try to answer this question, we need to understand that at a philosophical...

Algorithms in machine learning

So far we have developed an abstract understanding of machine learning. We understand the definition of machine learning which states that a task T can be learned by a computer program utilizing data in the form of experience E when its performance P improves with it. We have also seen how machine learning is different from conventional programming paradigms because of the fact that we do not code each and every step, rather we let the program form an understanding of the problem space and help us solve it. It is rather surprising to see such a program work right in front of us.

All along while we learned about the concept of machine learning, we treated this magical computer program as a mysterious black box which learns and solves the problems for us. Now is the time we unravel its enigma and look under the hood and see these magical algorithms in full glory.

We will begin with some of the most commonly and widely used algorithms in machine learning, looking...

Families of algorithms

There are tons of algorithms in the machine learning universe and more are devised each year. There is tremendous research happening in this space and hence the ever increasing list of algorithms. It is also a fact that the more these algorithms are being used, the more improvements in them are being discovered. Machine learning is one space where industry and academia are running hand in hand.

But, as Spider-Man was told that with great power comes great responsibility, the reader should also understand the responsibility at hand. With so many algorithms available, it is necessary to understand what they are and where they fit. It can feel overwhelming and confusing at first but that is when categorizing them into families helps.

Machine learning algorithms can be categorized in many ways. The most common way is to group them into supervised learning algorithms and unsupervised learning algorithms.

Supervised learning algorithms

Supervised learning refers to algorithms...

Summary

Through this chapter, we formally defined the concept of machine learning. We talked about how a machine learning algorithm actually learns a concept. We touched upon various other concepts such as generalization, overfitting, training, testing, frequent itemsets, and so on. We also learnt about the families of machine learning algorithms. We went through different machine learning algorithms to understand the magic under the hood, along with their areas of application.

With this knowledge we are ready to solve some real world problems and save the world.

The coming few chapters build on the concepts in this chapter to solve specific problems and use cases. Get ready for some action!