CourseraMachineLearning is a personal collection of resources, notes, and programming exercises from Andrew Ng’s popular Machine Learning course on Coursera. It consolidates lecture references, programming tutorials, test cases, and supporting materials into one repository for easier review and practice. The project highlights fundamental machine learning concepts such as hypothesis functions, cost functions, gradient descent, bias-variance tradeoffs, and regression models. It also organizes week-by-week course schedules with links to exercises, lecture notes, and additional resources. Alongside the official coursework, the repository includes supplemental explanations, code snippets, and references to recommended textbooks and external materials. By gathering course-related resources into a single space, this project acts as a practical study companion for learners revisiting or supplementing the original course.
Features
- Consolidated notes and resources from Andrew Ng’s Coursera ML course
- Programming exercise tutorials and test cases for Octave/MATLAB
- Week-by-week schedule of lectures and assignments
- Covers key ML concepts: regression, logistic regression, neural networks, SVMs, clustering, and recommender systems
- Additional references including online books and lecture notes from CS229
- Supplemental examples and explanations for self-study
Project Samples
