Deep Learning Course, Part 1 
Basic track

On the basic course in the first semester, you will be able to get acquainted with the basics of machine learning and neural networks, as well as master the tools of any data scientist. A significant part of the course will be devoted to an introduction to the Python language : both are necessary for learning neural networks.

At the end of the semester, students make an individual project

(project topics will be announced closer to the end of the course).

The course takes place on the Stepik platform. Registration is open until February 13th.

Training starts February 12th.

  1. Python Basics

  2. Libraries for Data Analytics: Numpy, Pandas, Matplotlib

  3. Mathematics for Data Science: linear algebra, mathematical analysis, optimization methods

  4. Fundamentals of machine learning, sklearn library

  5. Linear machine learning models,  OOP in data analysis

  6. Algorithm Compositions and Model Selection Methods

  7. Introduction to Neural Networks and PyTorch library

  8. Fundamentals of Convolutional Neural Networks

  9. Methods for designing and training neural networks

  10. Convolutional Neural Network Architectures

  11. Semantic segmentation and object detection

  12. Practical application of computer vision models

  13. Competitions on Kaggle

  14. Final project

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