ML-and-DS-Handbook

Compilation of things you should know as a Machine Learning Engineer and Data Scientist with TLDRs, longer explanations, and code snippets.

Structure:

• Math (Linear Algebra, Probability, Numerical Methods, etc.)
• Stats (Sampling, AB Testing, etc.)
• ML (Supervised ML, Unsupervised ML, etc.)
• Applied ML (Text Representations, Image Representations, etc.)
• Data (Databases, etc.)

Contributing

You can create a github issue or make pull-requests.

To make a pull-request:

• Make your changes in the ipython notebook

• I've uploaded the ipython notebook as markdown for better version control, so next, run this script: (but make sure you have nbconvert installed.)

sh convert_ipynb.sh

• Submit pull-request.

Note: All links to the notebook above link to an nbviewer version so that the notebook renders properly.