Machine Learning: here’s what it is
What a bad word I wrote? Right? You may have heard the word “Machine Learning” a few times during your life, especially in the last 5/10 years, or, it’s your first time and that’s why you came right away to inquire.
If so and you don’t know what Machine Learning is, I can tell you that you are in the right place to know everything you need to know about this subject.
Exactly, you read very well. Machine Learning (ML) is a subject! Know that this however, is just a branch of Artificial Intelligence which in turn is a child of computer science.
ML translated into Italian would mean machine learning and, as you can well understand from the word, this is just what it does since it is programmed for any algorithm.
So in simpler words this allows a machine (computer, robot etc…) to learn based on the feedback it receives. So on its own it is able to learn a lot in the field for which it was programmed.
Now though, before we see some practical examples where machine learning is applied, let’s find out what’s behind the name.
Suggested article: Convolutional Neural Network
What’s behind machine learning?
Maybe that’s what you’re wondering. Well, you need to know that behind such a strange and new word there is much more hidden!
In fact, there are subjects such as algebra and statistics behind Machine Learning and, these two certainly do not exclude logical reasoning and knowing how to code which is one of the foundations of ML if you want to program on a computer. Beyond that there are the various types of learning that ML can encompass:
What are the types of learning in ML?
We have as many as 4 types of learning that I will now try to explain to you as simply as possible.
- Supervised Learning: is the type of “guided” learning. The algorithm receives data, taken from a data set, called training data. Along with this data is also provided the output, that is, the result that the algorithm must obtain from the data taken. For this, the machine on its own will have to find the necessary function to obtain the expected output. Each time the algorithm makes a mistake, it learns from the error and recalculates in a different way, until it finds the solution.
- Semi-supervised Learning: in this type of learning, instead, the data provided to the computer is incomplete. That is, the machine receives some data with their respective output and some data without it. The task of this model is always to understand and find the appropriate function to get the output.
- Unsupervised Learning: as you can well guess, in this model, the results are not there (at least, they are not provided to the machine). In fact, here the task of the algorithm is to find the result by understanding the function to be applied based on the structure of the data provided as input.
- Reinforcement Learning: this type of algorithm is the most difficult to implement. The machine (robot or whatever) must take data based on the surrounding environment, in fact, it is provided with sensors and cameras capable of capturing data from dynamic images
Each error made, allows the machine to learn and correct, until the desired result is obtained.
It doesn’t end here, there is still more
There are other models of learning that an Artificial Intelligence can implement, for example we have the decision models called “Decision Tree” which provide various decisions based on input choices; the “clustering” or models that group data, similar information.
In addition there are also neural networks. The latter resemble very much the structure of human neural networks, because they collect data and return input on multiple layers, in fact, according to this we enter the field of Deep Learning a branch of Machine Learning.
But let’s not go into too much detail, otherwise with all these names you get nauseous, I can understand you…it was like that for me too at first.
Finally we get to the part that’s cool, the face of these ML algorithms.
Some real-world examples of Machine Learning: what are they?
You may not know it, and you may not expect it, but you live with these algorithms every day. They, your computer, smartphone, tablet, smartwatch and other accessories are equipped with Artificial Intelligence.
Even all the apps and online services you use on a daily basis!
Let’s take a few examples.
Facebook, Instagram, YouTube, Netflix and all of these types of apps have Artificial Intelligence and Machine Learning algorithms at the center of it all. For example, Netflix uses its algorithm to figure out what you like most so it can suggest you the ideal series or movie that you “wouldn’t want to miss.”
How do you do it?
Simply (at least in words), it collects data about you based on the movie or series you watched, time, actors, genre, etc.. So as to combine you with a similar user who has also seen something else and propose you then the content that suits you, or better to say “Recommended for you”.
Google translator or speech recognition learns from what you write and how you say it, so they can improve every day.
These are just a few of the examples, I could describe many more to you.
So that’s what ML does, learn from the available data and derive outputs by learning and correcting itself.
Machine Learning is one of those things that in the future will truly be present in everything and everyone’s life, every day.
How and where can you learn these skills?
Where and How is Machine Learning studied?
The subject matter I have told you about in this article is only one branch, so you have to start with a preliminary study which is Artificial Intelligence.
In fact, the discipline includes everything I mentioned in the article.
You can learn all of this with various degree programs, or by taking online courses where the path to follow is more direct. As of today, there are many courses both paid and free where you can learn all of this.
Here’s my advice: read this article on How to Study Artificial Intelligence in 2021.
