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ML Learning Material

This guide is intended for anyone having zero or a small background in programming, maths, and machine learning. If you don't like reading books, skip it. There is not a single way to become a machine learning expert and with motivation, you can absolutely achieve it.

All resources listed here are free, except some online courses which are certainly recommended for a better understanding, but it can certainly be done without it with a little more reading and practice. We recommend you to not pay for any of the course.

Don't be afraid to repeat videos or learn from multiple sources. Repetition is the key of success to learning!

Feel free to message us if there's any other great resources to add to this repository or regarding any doubts related to any material or concept

Table of Contents

No coding background, no problem we got you covered

Here is a list of some great courses to learn the programming side of machine learning.

Follow these steps for the next few weeks until the review 👇🏽

1. Setup a Anaconda Environment, which will help you complete the coding tasks

You can refer this video to set an environment: Setting up Conda Environment

2. Start with the Andrew Ng Course on Coursera

Complete the Week 1 and 2 of the Standford Course

This is the best way to start from nothing. Understand the mathematics behind each algortihm nicely, as you will be facing exercises on each algorithm and will be instructed to write algorithms from scratch.

3. Complete these coding task and build the algorithms from scratch

4. After you are done with the coding task follow free online courses on YouTube

Here is a list of awesome courses available on YouTube that you should definitely follow and are 100% free.

5. Read articles to enhance your knowledge

Here is a list of awesome articles available online that you should definitely read and are 100% free.

6. Read Books

Here are some great books to read for the people preferring the reading path.

7. Save Cheat Sheets!

Follow the news in the field!

References :

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A guide for Machine Learning

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