Machine Learning | Components of Machine Learning

 Machine learning is the study of computer algorithms that improve automatically through experience. It is seen as a part of artificial intelligence.

Three components of machine learning:

The greater variety in the samples you have, the easier it is to find relevant patterns and predict the result. Therefore, we need three components to teach the machine:

1. Data

Data Want to detect spam? Get samples of spam messages. The more diverse the data, the better the result. Tens of thousands of rows is the bare minimum for the desperate ones.

There are two main ways to get the data — manual and automatic.

* Manually collected data contains far fewer errors but takes more time to collect — that makes it more expensive in general.

* Automatic approach is cheaper — you're gathering everything you can find and hope for the best.

2. Features

When data stored in tables it's simple — features are column names. But what are they if you have 100 Gb of cat pics? We cannot consider each pixel as a feature. That's why selecting the right features usually takes way longer than all the other ML parts. That's also the main source of errors. Meatbags are always subjective. They choose only features they like or find "more important". Please, avoid being human.

3. Algorithms

Algorithms Most obvious part. Any problem can be solved differently. The method you choose affects the precision, performance, and size of the final model. There is one important nuance though: if the data is crappy, even the best algorithm won't help. Sometimes it's referred as "garbage in – garbage out". So don't pay too much attention to the percentage of accuracy, try to acquire more data first.

The Map of the Machine Learning World

If you are too lazy for long reads, take a look at the picture below to get some understanding.

Always important to remember — there is never a sole way to solve a problem in the machine learning world. There are always several algorithms that fit, and you have to choose which one fits better. Everything can be solved with a neural network, of course, but who will pay for all these GeForces?

Let's start with a basic overview. Nowadays there are four main directions in machine learning.