Machine Learning: what is it?

Machine Learning: what is it?

Machine learning is one of the branches of artificial intelligence based on training algorithms on data sets. Analyzing the solutions to many similar problems, information systems begin to independently identify patterns and offer solutions.

How machine learning works

Machine learning requires several stages: preparation of a data set, training of algorithms on this array (training itself), evaluation of their work and correction of algorithms.

Preparing a data set includes collecting data from sources related to the task, then clearing the data and creating a sample. A sample may be needed because the array is too large, and a small part of the data is enough to solve the problem. In this case, there is a sample itself that will be used by the learning algorithm and a control sample that will be used to evaluate the result.

* Training is the stage at which a mathematical function is selected that allows you to solve the problem. Depending on which machine learning model is chosen – with a teacher, without a teacher or so-called deep learning – this process will be different.

* Evaluation – after graduation, the correctness and effectiveness of the algorithms are evaluated on another sample of data allocated at the preparation stage.

* Correction – at this stage, the algorithms are refined to make them more accurate, more efficient and more compact. Then the process is repeated.

There are three components of machine learning (ML):

Data. If we want to predict the weather, we need a summary of the weather over the past few years (the more, the better). We want to identify spam, we need examples of such letters. The better the data, the more effective the program will work.

Signs. This is a set of properties, characteristics or features that describe our model. If we talk about the weather, it’s temperature, wind speed, season. In the case of spam, these are senders, subject of letters, certain phrases and images. Properly selected signs are the key to successful learning.

Algorithm. It’s simple. Each problem can be solved in different ways. Different algorithms can be selected for different purposes.

But still, the main thing in ML is data. No matter how perfect the algorithm of work is, if the quality of the data is not very good, the result will be appropriate.

Machine learning methods

How does machine learning work? Artificial intelligence is similar to a small child to whom parents explain why the sky is blue and the grass is green. He also learns the world on his own by trial and error.

There are many teaching methods, each of which includes different algorithms. Let’s talk about the most common basic methods:

* classical training;

* reinforcement training;

* neural networks and deep learning.

Let’s briefly run through each of them.

Classical training

Most AI uses classical learning. These are simple algorithms based on patterns in the data.

There are two types of classical training:

* with a teacher (supervised learning);

* unsupervised learning.

Teaching with a teacher

The principle is simple. We train the car using real examples. Let’s say we want to teach her how to distinguish apples from pears. We upload data set to the program and tell it that these pictures show apples, and these pictures show pears. And she, in turn, finds common features, analyzes them and builds connections.

If we give the car a picture without a description, it must correctly determine what kind of fruit is depicted on it based on the data obtained.

Therefore, it is important to select the correct data for training and load it as much as possible: the more data we download, the more accurate and faster the definition will occur.

Unsupervised learning

This method is used when it is not possible to provide the robot with marked data. The program itself finds patterns, common features and classifies the data obtained.

Unsupervised learning is great for clustering (segmentation) of data. It is often used in targeted advertising. When the user’s actions or preferences cannot be classified in advance.

Reinforcement training

Let’s take the Snake game as an example. On the field there is an object that the snake needs to reach, but she herself does not know how to do it and which way is the most effective, she only knows the distance to the object. By trial and error, the snake finds the best option for movement and analyzes situations that lead to loss. If you have any questions you can contact with machine learning consulting company.

This method is also used to train robot vacuum cleaners or self-driving cars. Learning is like a game: the car receives a point for the right decision, points are deducted for mistakes.

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