Hi there! A while ago, I have been talking to a friend and the topic touched on Machine Learning. And by the end of the conversation, I concluded that many people have some prejudice against the whole topic of “intellectual machines”, or even might be afraid to start the learning process due to the overwhelming amount of information. So,
I decided to break the ice for everyone who is just starting out on this journey of mastering the Machine Learning.
Let us be clear — Machine Learning is not Magic. Machine Learning (so-called ML) is the study of computer algorithms that improve automatically through experience. Generally speaking, ML is a subfield of computer science, and it is highly connected to Data Science. These fields combine mathematical, computing, statistical, and presenting skills. That is a form of logic and structure.
In this series of tutorials, I will help you to dive into the Machine Learning field. With all of the great developments and the variety of information in this field, it might be extremely difficult to actually start if you are a complete beginner. So, I decided to create a short series of lessons for everyone passionate about ML.
The main idea here, is to explain things in a simple language so even a child could understand it and start practicing Machine Learning (yes, we are all children inside our hearts, right?).
In this part, we will briefly cover the reasons for learning ML, go through general terms/definitions, explore which common Python libraries are used for Data Science and Machine Learning, and load our first dataset!
In other words, we are preparing ourselves for the Machine Learning journey.
Why Machine Learning?
Machines are great workers but they are generally dumb. Because they do only, and the only task the programmers/engineers tell them to do. No less and no more than the given instructions to follow, or explicit steps to take.
For simple tasks, it is easier to program a machine to do its job well. But for complex problems that involve many steps, it is not a trivial task.
What if machines could actually learn on their own, without being explicitly programmed to do so? This question is also the answer to why this field is so popular. Especially, nowadays, when the computational power is enough to transform some of the ideas into real life.
To sum up, Machine Learning is the branch of Artificial Intelligence that covers the statistical part of it. It teaches the computer to solve problems by looking at hundreds (or thousands) of examples, learning from them, and then using that experience to solve the same problem in new situations.
I hope that this satisfies your curiosity. As I value both my and your time, I would like to move on, and start with the actual hands-on examples.
But first thing first.
To be sure we are all on the same page, let us briefly go through the common terms and definitions used in the fields of Data Science and Machine Learning.