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A collection of notes and implementations of machine learning algorithms from Andrew Ng's machine learning specialization.

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Machine Learning Specialization with Andrew Ng

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

Programming Assignments

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

Notes

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.

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A collection of notes and implementations of machine learning algorithms from Andrew Ng's machine learning specialization.

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  • Jupyter Notebook 100.0%