So how exactly do you get started with machine learning? The first step is to make sure you understand the basics of your language of choice, and maybe even more than the basics, kind of like the intermediate concepts involved with that language, like objects and classes and all of that. In this article, for example, I have recommended Python. If you are going to use that, make sure you know how Python works, the syntax e.t.c because these beginning steps involve understanding Python or your preferred language to pick out your data, clean it, select it, and work on the model.
After going through the first step, it is now okay to jump into machine learning. It is easy for beginners to lose track and try to go into the deep concepts right off the bat, but I always preach the basics. Learn the basic algorithms first. Now, this is not going to be fun at first. Most people do not brag about using these algorithms, but they are important to understand. They are fundamental, especially if you want to understand how things work and progress in the correct order.
The first algorithm I recommend you learn as a beginner is called linear regression. It is a simple algorithm, and you will understand how this works based on some simple math principles. The next thing that I suggest you should learn after linear regression is K-Nearest-Neighbors ( KNN ). This is a more advanced and useful algorithm. KNN is used for a lot of different applications. I mean, even some of the most advanced AIs use it to do specific things like clustering information. When you get into this, you will notice that it is a different flavor from linear regression. Who doesn’t like different flavors? Anyway, the next thing to learn is Support Vector Machines ( SVM ). These are more advanced because they deal with high dimensional data, and this is where your math will be tested a little bit.