This repository contains a collection of notes and implementations of machine learning algorithms from Andrew Ng's machine learning specialization. The specialization consists of three courses:

1. Supervised Machine Learning: Regression and Classification

2. Advanced Learning Algorithms

3. Unsupervised Learning, Recommenders, Reinforcement Learning

Lab assignments are completed using Jupyter Notebooks and Python. Any code I wrote is marked with "START CODE HERE, END CODE HERE", so it can be removed easily if wanting to complete the labs from a clean state.

1. Linear Regression
2. Logistic Regression
3. Multiclass Classification and Neural Networks
4. Neural Networks for Multiclass Classification
5. Advice for Applying Machine Learning
6. Decision Trees
7. K-means Clustering
8. Anomaly Detection
9. Collaborative Filtering Recommender Systems
10. Content-based Filtering Recommender Systems
11. Reinforcement Learning

These notes are information I found helpful while studying through the curriculum. They include high level overviews, practical tips, and lots of walkthroughs through core mathematical concepts.

The idea is that most of the course is covered using these notes in conjunction with the assignments. Though, I highly suggest using Andrew's video series (free) and optional labs (paid), as he does a fantastic job of teaching.

I originally wrote these notes in Notion, which I've provided links for below. PDF versions of these are uploaded into the notes folder of the repo for accessibility. I suggest working through the collection in the order they are provided, as much of the knowledge builds upon itself.

• Linear Regression
• Gradient Descent
• Logistic Regression
• Neural Networks
• Practical Machine Learning
• Decision Trees
• Tree Ensembles
• Clustering
• Anomaly Detection
• Recommender Systems
• Principle Component Analysis
• Reinforcement Learning