Stanford CS229 Machine Learning in Python

This repository contains the problem sets for Stanford CS229 (Machine Learning) on Coursera translated to Python 3. It also contains some of my notes.

Check out the course website and the Coursera course. Please note that your solutions won't be graded and this repo is not affiliated with Coursera or Stanford in any way.

Installation

Make sure you have jupyter notebooks installed. You can find instructions here.

The following Python packages are used:

• Numpy
• Scipy
• Matplotlib
• Pandas
• Pillow
• Natural Language Toolkit

You can install all dependencies using:

python3 -m pip install -r requirements.txt

Instructions

1. Please download the exercises (pdf) from the Coursera course. Some instructions are included in the Notebooks.
2. Complete the exercises in the exercises Notebook.
3. Compare your answers to the code in solutions Notebook.

Contents

1. Linear Regression
2. Logistic Regression & Regularization
3. Multiclass Classifcation & Neural Networks
4. Neueral Networks Learning
5. Regularized Linear Regression and Bias v.s. Variance
6. Support Vector Machines
7. K-means Clustering and Principal Component Analysis
8. Anomaly Detection and Recommender Systems

Copyright Notice

All code, exercises, data and other files in this repo are ©Stanford University. If you are unhappy about me hosting these files on GitHub for educational purposes, please send me an email.

The code was 'translated' to Python by Rick Wierenga. Some of the instructions are modified to better fit the Python ecosystem by me too. The data, background information and the intended exercise are the same.

Solutions

At the request of Stanford staff, I removed the notebooks with solutions from this repository. The exercises and notes are still available.

©2020 Rick Wierenga