This is the guide for Andrew Ng's ML course programming assignment done in Python, adapted from the original guide written for Octave or MATLAB.

Andrew Ng's Machine Learning course is a good place to start learning about ML since it's not too technical on the programming side and builds your foundation by explaining the maths behind. And yet, the explanation is not too jarring; you just need to review linear algebra and you're good to go.

The course's programming assignment is made for Octave or MATLAB only, which is why I compiled this guide so it's ready to use in Python. The guide is written in LaTeX to help familiarize myself with LaTeX then compiled to pdf.

For each assignment, you will need:

• notebook for the code, in ipynb file
• the assignment guide, in pdf file
• database files, usually in txt file (read the guide for the exact file names)

The rest of the files are auxiliary files to compile the pdf, which you won't need to complete the assignment.

1. Linear Regression
2. Logistic Regression
3. Multi-class Classification and Neural Networks
4. Neural Networks Learning (skipped, too hard...)
5. Regularized Linear Regression and Bias vs Variance
6. Support Vector Machines