We will analyze applied tasks from various fields of data analysis: text analysis and information search, collaborative filtering and recommendation systems, business analytics, time series forecasting. Using their example, you will learn how to extract features from heterogeneous data, what problems arise and how to solve them. You will learn how to reduce the customer's task to a formal statement of the machine learning problem and understand how to check the quality of the constructed model on historical data and in an online experiment. On each task, we will study the pros and cons of the machine learning algorithms passed.