Machine Learning & Data Science with Python basic course


Welcome to Machine Learning & Data Science with Python basic course. The main purpose of this lookout course is to encourage students to apply basic knowledge of statistics, mathematics and Python to start solving real world problems using open source Machine Learning tools. The course is divided in two different parts.

Firstly, the basics of Data Science with Python are obeserved, as general data analysis, its processing and understanding along with the knowledge of how to represent information effectivly is crucial for any machine learning practioner. What is more, sometimes its beneficial to firstly try some statistical methods of solving the particular problem, before diving into more complicated ML stuff. In addition some open source packages for working with data and scientific calculus are introduced:
As the second part of course, students will have a hands on introduction to basic machine learning algorithms and core machine learning concepts. The course won’t dig in depth of the algorithms work, but we strongly encourage you to checkout useful references that can be found throughout this course. Finally, the students will have a chance to work with such packages:


Currently the course provides machine learning assignments based only on scikit learn package.

Notes about the course

In this course we will use Python 3.7 and Jupyter Notebook as main tools for conducting exercises. All the work will be conducted on Google Colaboratory, as it’s free to use powerfull tool based on Jupyter Notebook, thus you will need a google account to work on this platform. If you’re not familiar with Jupyter Notebook, fill free to checkout this tutorial. The course is VNTU students’ oriented, but everyone can access the notebooks in Colaboratory and work through the course, if you want to do exercises on your local machine you can find the related information in FAQ.


Currently the course doesn’t provide any testing of the exercises.

Basic knowledge requirments

The course requires basic knowledge of Python along with its core concepts. In addition an intermediate knowledge of English or at least understanding of technical vocabulary is mandatory.