Why are there so many machine learning techniques? The thing is that different algorithms solve various problems. The results that you get directly depend on the model you choose. That is why it is so important to know how to match a machine learning algorithm to a particular problem.
In this post, we are going to talk about just that. Let’s get started.
Variety of machine learning techniques
First of all, to choose an algorithm for your project, you need to know about what kinds of them exist. Let’s brush up your knowledge of different classifications.
Algorithms grouped by learning style
It’s possible to group the algorithms by their learning style.
Supervised learning
In the case of supervised learning, machines need a “teacher” who “educates” them. In this case, a machine learning specialist collects a set of data and labels it. Then, they need to communicate the training set and the rules to the machine. The next step is to watch how the machine manages to process the testing data. If there are some mistakes made, the programmer corrects them and repeats the action until the algorithm works accurately.
Unsupervised learning
This type of machine learning doesn’t require an educator. A computer is given a set of unlabeled data. It is supposed to find the patterns and come up with insights by itself. People can slightly guide the machine along the process by providing a set of labeled training data as well. In this case, it is called semi-supervised learning.
Reinforcement learning
Reinforcement learning happens in an environment where the computer needs to operate. The environment acts as the teacher providing the machine with positive or negative feedback that is called reinforcement.
You can find a more detailed explanation about these techniques in our post on the difference between AI and machine learning.
Machine learning techniques grouped by problem type
Another way to divide the techniques into groups is based on the issues they solve.
In this section, we will talk about classification, regression, optimization, and other groups of algorithms. We are also going to have a look at their use in industry. For more detailed information about every common machine learning algorithm, check out our post about machine learning algorithm classification.
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