Automated machine learning (AutoML): algorithms and tools to light the entry threshold

This article explores the concept of automatic machine learning (AutoML), which aims to automate the processes of model selection, tuning, and evaluation. Focuses on key AutoML algorithms and approaches such as model search, hyperparameter tuning, data preprocessing, and feature engineering. Describes popular AutoML tools, including Auto-sklearn, TPOT, H2O.ai AutoML, and Google Cloud AutoML, and their role in simplifying the process of developing machine learning models. Examples of the use of AutoML in various industries such as medicine, finance and marketing are provided. The article also discusses the prospects for the development of AutoML and its potential impact on the spread of machine learning technologies in various areas of life.