A Foundation For Machine Learning and Data Science

This course is designed by an industry expert who has over 2 decades of IT industry experience including 1.5 decades of project/ program management experience, and over a decade of experience in independent study and research in the fields of Machine Learning and Data Science.

The course will equip students with a solid understanding of the theory and practical skills necessary to learn machine learning models and data science.

When building a high-performing ML model, it’s not just about how many algorithms you know; instead, it’s about how well you use what you already know.

Throughout the course, I have used appealing visualization and animations to explain the concepts so that you understand them without any ambiguity.

This course contains 9 sections:

     1. Introduction to Machine Learning

     2. Anaconda – An Overview & Installation

     3. JupyterLab – An Overview

     4. Python – An Overview

     5. Linear Algebra – An Overview

     6. Statistics – An Overview

     7. Probability – An Overview

     8. OOPs – An Overview

     9. Important Libraries – An Overview

This course includes 20 lectures, 10 hands-on sessions, and 10 downloadable assets.

By the end of this course, I am confident that you will outperform in your job interviews much better than those who have not taken this course, for sure.