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Machine learning (ML) is a branch of artificial intelligence. In more detail, it is a technique for analyzing data that allows a machine/robot/analytic system to learn on its own by solving an array of similar problems.

To simplify, machine learning technology is about finding patterns in an array of presented information and choosing the best solution without human involvement.

The popularity of AI technology is growing, which means that the demand for it is also growing. This leads to an increase in the entire developer community and the emergence of AI frameworks that simplify training and work.

ML-based frameworks are sets of tools and standard implementations to enable faster development of a software product. It is often confused with a library, which, in turn, is a set of standard implementations, functions, and data structures that facilitate solving a problem.

Many experts believe that artificial intelligence is in a transitional stage between the second and third levels. That is, IT-savvy people are already using innovations, while the majority are still afraid. Without experienced machine learning developers or experts in data science, it is impossible to use machine learning competently for business

A machine learning engineer is a programmer who uses special data sets and algorithms to train artificial intelligence.

What a Machine Learning Specialist Does


The tasks of an ML development team vary from company to company and from project to project, but the most common things a machine learning developer does are this:

  1. Collects and prepares data. You need a lot of data to pump artificial intelligence. And not any data, but specially marked-up data. For example, to teach a machine to distinguish cats from dogs, you have to give it a lot of photos and “sign” which ones show cats and which ones show dogs. This classification of data is called tagging. Collecting data manually is difficult: if it is, for example, images, you need hundreds of thousands of photos from different angles and with different lighting conditions.
  2. Builds machine learning models for data processing. A simple example is a smart social media feed. To show only interesting posts, the algorithm tracks your likes, comments, friends’ preferences, and even people similar to you in interest, and then shows potentially interesting content. ML specialists create and train such algorithms. The result of their work is a clever model that produces predictions based on the data. If your tastes change, if you subscribe to someone or comment on a post, your feed will change.

5 Benefits ML Can Bring To The Business

American retail chain Target quickly realized that by using machine learning in business it is possible to predict not only the behavior of a customer but also the imminent changes in his life. The company’s algorithms are very finely tuned.

Machine learning and AI are slowly changing retail. Today they can automate the monitoring of competitors’ prices, they can predict demand and sales, and they make analysts develop new strategies. Any improvement in the accuracy of a particular function opens up many benefits, especially with regard to business.


Ten years ago, analysts used spreadsheets to collect, analyze, and change prices. All this was extremely slow and inefficient and led to an incredible amount of human error.

And today retail giants such as Walmart, Amazon, and DNS-shop are outsourcing all calculations to self-learning algorithms. Because they not only deal with huge amounts of data but also remember all the successful and unsuccessful experiments that cost the business money. And only on the basis of this information do algorithms offer the best pricing decisions.

Here are 5 main business benefits of machine learning:

1. Predicting client behavior.

2. Reducing routine tasks.

3. Expanding sales capabilities.

4. Business Analytics.

5. Increased client happiness.

Businesses benefit from the machine learning resources available: for analyzing the actions of site users, their previous purchases, and product selection, predicting the likely financial results of business decisions, finding the target audience for a project, and automating processes.

Machine learning development is the future. There are already emerging companies that are developing prostheses for paralyzed people that are controlled via a neuro interface. Diagnosing early on diseases, organizing traffic in cities, and checking the performance of systems with drones instead of humans are all making life more convenient and easier.

Machine learning technology is the search for patterns in the array of presented information and the selection of the best solution without human involvement. Ask someone with a passion for robotics about the scope of machine learning. You will hear a lot of fantastic stories. For example, robots will train themselves to do human tasks. To extract minerals in the Earth’s interior, drill oil and gas wells, explore the depths of the ocean, extinguish fires, and so on. The programmer will not have to write massive and complex programs for fear of making a mistake in the code. The robot, thanks to MO, will learn how to behave in a specific situation based on data analysis.

Great, but so far fantastic. In the future, maybe even in the not too distant future, it will become reality.

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