Machine Learning Crash Course Starter Guide

This guide provides technical and mathematical background required for Google's Machine Learning Crash Course(MLCC) to beginners.

Why?

MLCC is great but sometimes people get stuck for weeks just because they don't have basic background in mathematics and python required for MLCC. Here we're trying to help you kickstart your journey with Machine Learning.

How to use this guide?

You can either download whole repository and access it offline using Jupyter notebook, which comes preinstalled with Anaconda. OR you can access notebooks using View in Colaboratory link in the starting of notebooks.

Found this guide helpful?

Then please give a star and contribute to make it more informative.

Progress

• Math
• Basic Python iPython Notebook
• Advance Python iPython Notebook (numpy, matplotlib, seaborn)

Future Goals

• Basic ML algorithms with practical examples
• Case studies
• Deploying ML models

Source

We are very thankful to amazing teachers and educators from all around the world. Major resources used to create this guide:

• Khan Academy
• Math is Fun

Note: If your content is used here, please feel free to mail me at pratik97.work@gmail.com to add your name / organization name in this list.

I wanna add a topic to this guide, how to submit it?

Please checkout the list of issues and create an issue, if it's not there. You can submit a Pull Request, if you've something to share with the community.

Good Luck

Keep Coding... Keep Rocking...