What you'll learn

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 expent.

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

Description

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.




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