Welcome to the Stanford Machine Learning course repository! This course, expertly led by Professor Andrew Ng, is your comprehensive guide to mastering machine learning. Through engaging lectures, hands-on exercises, and insightful projects, you'll acquire the essential skills and knowledge to apply machine learning techniques effectively to real-world challenges.
In this repository, you'll find a collection of resources that complement the course material. For a more immersive experience, including access to lecture slides and video lectures, make sure to visit the Coursera course page. Dive in and start your journey towards becoming proficient in machine learning!
For an enhanced learning experience, I highly recommend visiting this complementary notes website.
| Week # | Description | Notes |
|---|---|---|
| Week 1 | Introduction to machine learning. |  |
| Week 2 | Linear Regression with One Variable. | |
| Week 3 | Linear Algebra - review. | |
| Week 4 | Linear Regression with Multiple Variables. | |
| Week 5 | Octave | - |
| Week 6 | Logistic Regression. | |
| Week 7 | Regularization. | |
| Week 8 | Neural Networks - Representation. | |
| Week 9 | Neural Networks - Learning. | |
| Week 10 | Advice for applying machine learning techniques. | |
| Week 11 | Machine Learning System Design. | |
| Week 12 | Support Vector Machines. | |
| Week 13 | Clustering. | |
| Week 14 | Dimensionality Reduction. | |
| Week 15 | Anomaly Detection. | |
| Week 16 | Recommendation Systems. | |
| Week 17 | Large Scale Machine Learning. | |
| Week 18 | Application Example - Photo OCR. | |
Explore my solutions to hands-on programming exercises to solidify your understanding of the concepts taught in the course.
| # | Title | Solution |
|---|---|---|
| 1 | Linear Regression. |  |
| 2 | Logistic Regression. | |
| 3 | Multi-class Classification and Neural Networks. | |
| 4 | Neural Network Learning. | |
| 5 | Regularized Linear Regression and Bias vs Variance. | |
| 6 | Support Vector Machines. | |
| 7 | K-means Clustering and Principal Component Analysis. | |
| 8 | Anomaly Detection and Recommendation Systems. | |
We encourage contributions that enhance the repository's value. To contribute:
- Fork the repository.
- Create your feature branch (
git checkout -b feature/AmazingFeature). - Commit your changes (
git commit -m 'Add some AmazingFeature'). - Push to the branch (
git push origin feature/AmazingFeature). - Open a Pull Request.
This project is licensed under the MIT License - see the LICENSE file for details.