Nowadays, computers draw paintings, algorithms make up poems and classify photos, robots independently move around the environment, and some of them are able to make their own decisions. Everything mentioned above is possible with the help of machine learning.

Let’s find out what machine learning is, how it works and where it is used nowadays.

What is machine learning?

Machine learning (ML) is one of the artificial intelligence methods that teaches the computers to complete tasks on their own. Computers conduct analytical work defining conformities faster than people because of files loaded in advance and special algorithms.

Algorithm selection depends on the task necessary to solve and type of data developers are working with. Algorithms are provided with the learning data set to process various requests.

As a rule, computers need much information and statistics in order to create correct and efficient assessments.

Machine learning classification

Classic learning

Classical learning can be both with or without a teacher. When a machine is trained by the teacher, it receives formatted data. Thus, the computer can return results quicker.

If the algorithm is not supported by the teacher, it has to analyze the information itself, searching the conformities. Such approach takes more time, still the developers do not need to have the database beforehand.

Reinforcement learning

Reinforcement learning is used to teach robots to survive in different environments and adapt to new conditions. This method is also applied for teaching game characters and robots. They have to generalize the situation, taking advantage of it.

Ensemble learning

Using this method, developers gather several machines with different learning approaches together. Next, machines are trained to correct the errors of each other.

Deep learning

Deep learning is based on the neural networks that complete tasks of computer vision, speech recognition and machine translation.

Importance of ML: use cases and benefits

Machine learning powers many services we use today, such as:

• voice recognition assistants;
• handwriting recognition;
• language recognition;
• online recommendersystems;
• search services;
• suspicious transactions identification;
• exchange rate forecast;
• demand analysis;
• smart devices learning.

Machine learning technology develops many different industries including business, healthcare, finances, manufacture and IT sphere.

Target

Target, the American retail network, uses machine learning for prediction of consumer behavior. Based on the purchase information, algorithms determine the changes in lifestyle, interests and needs of consumers, in order to show them ads of products they require.

Google

In the spring of 2019, Google corporation opened a new robotics lab focused on machine learning.

Google teamed up with the researchers from MIT and other universities to create TossingBot robot that teaches itself using experience instead of machine learning. It is able to pick up and toss different objects into the right containers.

Furthermore, Google is engineering a robot that will adapt to new scenarios by using machine learning.

The Future of Machine Learning

Machine learning experts predict that investments into technology development will comprise approximately $58 billion by 2021. As the result, much more complicated neural networks will be developed for smarter devices with new features.

Specialists agree that voice assistants will be more efficient, computer security systems will become more advanced, self-driving cars will navigate temselves and reduce the traffic jams, and patients will receive individual medical care.