Foundations of Data Science & Machine Learning For Free

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Ruchika oberoi

Staff member
Mar 27, 2022

Foundations of Data Science & Machine Learning Download For Free


What you’ll learn

  • Learn the essentials - the three main pillars of data science and ML - Programming, Math, and Statistics.
  • Everything from basic data structures to data extraction using python programming. Learn to work with data libraries: NumPy, Pandas, Matplotlib, and Seaborn.
  • How linear algebra and calculus underpin the training of ML models.
  • How Statistics enables you to describe data and quantify uncertainty in an experiment.
  • Cover all pre-requisites and pre-work before starting any Google’s(or any) data science or ML program.
  • Build models from scratch, learn the math behind, program


  • A computer (Windows/Mac/Linux). You must know basic school-level arithmetics. That’s it! No previous coding experience is needed. All tools and software used in this course will be free.


To have a successful, long-lasting career in Data Science or Machine Learning, you’ll need a solid understanding of the three pillars of DS and ML namely, Programming, Math, and Statistics.

The course is based on Google’s recommendations before starting any ML course.

It is a comprehensive yet compact course that not only covers all the essentials, pre-requisites, & pre-work but also explains how each concept is used computationally and programmatically (python).

We follow the following path in this course:

  • Learn to set up a professional python environment
  • Learn to program in python using fundamental data structures and methods.
  • Learn to work with data science libraries
  • NumPy for Multidimensional Arrays
  • Pandas for Data Manipulation
  • Matplotlib and Seaborn for Data Visualization
  • Basics of Algebra - From variables to all important functions
  • Linear Algebra for Machine Learning - data representation, vector norms, solving linear regression problems.
  • Calculus that trains ML models - learn how gradient descent works to minimize the loss function.
  • Training a linear regression model from scratch without using any ML package
  • Statistics, data distributions, and basics of probability
After completing this course, you’ll be ready to straight away start working on:

  • Data Analysis projects
  • Pick up any ML course
  • Start with a Data Science course
  • Start with the Predictive analytics course
  • Enroll for any fast-paced Bootcamp course after covering all the basics.

Who this course is for:

  • Anyone looking to get into data science or ML. This is where one should start.

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