Stanford CS229 Machine Learning in Python

This repository contains the problem sets as well as the solutions for the Stanford CS229 - Machine Learning course on Coursera written in Python 3. Some additional notes taken by me are also included.

Please check out the course website and the Coursera course.

Please note that your solutions won't be graded and are not affiliated to Coursera in any way. If your answers differ from mine and you argue that yours are better, please create an issue on GitHub.

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.

©2020 Rick Wierenga