Learn Python with Google Colab – A Step to Machine Learning

Basics of Python

Data types

Printing output

If-Else conditions

Looping using for, while

Arithmatic operations

Working with functions

Working with List and Arrays

Performing action on List

Tuple, Set and Dictionary

Working with packages

Hands-on with Class


  • Basic programming concepts are sufficient


This course is completely practical based and is per-requisite for our upcoming Machine Learning course. With around 25 lectures, this course is designed in such a way that you can take spark of Google Colab enabling Jupiter notebook , the best platform to practice Machine Learning  and is enriched with all the basic concepts that is required to start with python programming. After completing this course, you should have basic python understanding.

Who this course is for:

  • Beginner and Intermediate

Course content

R and Python coding with Prython

Learn how to use Prython for coding both R and Python projects

Design complex data science projects in Prython


  • Know some Python and/or R
  • Basic knowledge about data science and analytics


In this course we will learn how to use Prython, which offers a different way of coding than existing R/Python IDEs. It allows us to drop our code into panels that we place and connect in a canvas. In a normal IDE your code will run linearly from start to end, making it really hard to create sub-experiments/tests, and also to organise your project clearly. In Prython each panel accepts multiple IN and OUT connections, effectively transforming it into a 2D Jupiter notebook. It also has a wide array of tools that complement this canvas functionality: such as displaying dataframes next to the panels that modified them, allowing you to freeze your outputs, attaching consoles, navigation markers, etc.

We assume that the student is already familiar with R or Python, and some familiarity with matplotlib, scikit-learn,or keras would be beneficial as well.

Who this course is for:

  • Python and R practitioners with a focus on data science
  • ML engineers
  • Statisticians, engineers, and economists designing statistical models

Course content