I am sure a number of you have heard about machine learning. A dozen of you might even know what it is. And a couple of you might have worked with machine learning algorithms too.
You see where this is going? Not a lot of people are familiar with the technology that will be absolutely essential 5 years from now. Siri is machine learning. Amazon’s Alexa is machine learning. Ad and shopping item recommender systems are machine learning.
Let’s try to understand machine learning with a simple analogy of a 2 year old boy. Just for fun, let’s call him Kylo Ren
Let’s assume Kylo Ren saw an elephant. What will his brain tell him ?(Remember he has minimum thinking capacity, even if he is the successor to Vader). His brain will tell him that he saw a big moving creature which was grey in color. He sees a cat next, and his brain tells him that it is a small moving creature which is golden in color. Finally, he sees a light saber next and his brain tells him that it is a non-living object which he can play with!
His brain at this point knows that saber is different from the elephant and the cat, because the saber is something to play with and doesn’t move on its own. His brain can figure this much out even if Kylo doesn’t know what movable means. This simple phenomenon is called Clustering .
Machine learning is nothing but the mathematical version of this process.
A lot of people who study statistics realized that they can make some equations work in the same way as brain works.
Brain can cluster similar objects, brain can learn from mistakes and brain can learn to identify things.
All of this can be represented with statistics, and the computer based simulation of this process is called Machine Learning. Why do we need the computer based simulation? because computers can do heavy math faster than human brains.
I would love to go into the mathematical/statistical part of machine learning but you don’t wanna jump into that without clearing some concepts first.
Let’s get back to Kylo Ren. Let’s say Kylo picks up the saber and starts playing with it. He accidentally hits a stormtrooper and the stormtrooper gets injured. He doesn’t understand what’s going on and continues playing. Next he hits a cat and the cat gets injured. This time Kylo is sure he has done something bad, and tries to be somewhat careful. But given his bad saber skills, he hits the elephant and is absolutely sure that he is in trouble.
He becomes extremely careful thereafter, and only hits his dad on purpose as we saw in Force Awakens!!
This entire process of learning from your mistake can be mimicked with equations, where the feeling of doing something wrong is represented by an error or cost. This process of identifying what not to do with a saber is called Classification .
Clustering and Classification are the absolute basics of machine learning. Let’s look at the difference between them.
Kylo differentiated between animals and light saber because his brain decided that light sabers cant move by themselves and are therefore, different. The decision was based solely upon the objects present (data) and no external help or advice was provided.
In contrast to this, Kylo differentiated the importance of being careful with light saber by first observing what hitting an object can do. The decision wasn’t completely based on the saber, but on what it could do to different objects . In short, there was some help here.
Because of this difference in learning, Clustering is called an unsupervised learning method and Classification is called a supervised learning method.
They are very different in the machine learning world, and are often dictated by the kind of data present. Obtaining labelled data (or things that help us learn , like stormtrooper,elephant and cat in Kylo’s case) is often not easy and becomes very complicated when the data to be differentiated is large. On the other hand, learning without labels can have it’s own disadvantages , like not knowing what are the label titles.
If Kylo was to learn being careful with the saber without any examples or help, he wouldn’t know what it would do. He would just know that it is not suppose to be done. It’s kind of a lame analogy but you get the point!
We are just getting started with Machine Learning. Classification itself can be classification of continuous numbers or classification of labels. For instance, if Kylo had to classify what each stormtrooper’s height is, there would be a lot of answers because the heights can be 5.0, 5.01, 5.011, etc. But a simple classification like types of light sabers (red,blue.green) would have very limited answers. Infact they can be represented with simple numbers. Red can be 0 , Blue can be 1 and Green can be 2.
If you know basic math, you know that 0,1,2 and 5.1,5.01,5.011 are different and are called discrete and continuous numbers respectively. The classification of discrete numbers is called Logistic Regression , and classification of continuous numbers is called Regression.
Logistic Regression is also known as categorical classification, so don’t be confused when you read this term elsewhere
This was a very basic introduction to Machine Learning. I will dwell into the statistical side in my next post. Please let me know if I need any corrections :)
Second part posted here.
