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https://github.com/djeada/stanford-machine-learning
Welcome to my collection of notes for the Stanford Machine Learning course, led by Professor Andrew Ng. These notes are a compilation of insights, key takeaways, and important concepts I gathered while studying the course material.
https://github.com/djeada/stanford-machine-learning
andrew-ng coursera machine-learning
Last synced: 3 days ago
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Welcome to my collection of notes for the Stanford Machine Learning course, led by Professor Andrew Ng. These notes are a compilation of insights, key takeaways, and important concepts I gathered while studying the course material.
- Host: GitHub
- URL: https://github.com/djeada/stanford-machine-learning
- Owner: djeada
- License: mit
- Created: 2021-04-05T23:44:54.000Z (almost 4 years ago)
- Default Branch: main
- Last Pushed: 2024-08-28T13:46:47.000Z (5 months ago)
- Last Synced: 2024-08-28T15:11:39.891Z (5 months ago)
- Topics: andrew-ng, coursera, machine-learning
- Language: Jupyter Notebook
- Homepage: https://adamdjellouli.com/pages/stanford_machine_learning
- Size: 38.3 MB
- Stars: 2
- Watchers: 4
- Forks: 2
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# Stanford-Machine-Learning
Welcome to my collection of notes for the Stanford Machine Learning course, led by Professor Andrew Ng. These notes are a compilation of insights, key takeaways, and important concepts I gathered while studying the course material.
While this repository is not the official course repository, it serves as a supplement to the course for those looking to review or reinforce their understanding. For full access to the course's lectures, slides, and more, be sure to visit the [Coursera course page](https://www.coursera.org/learn/machine-learning). Feel free to explore these notes as you embark on your own machine learning journey!
## Course Outline and Resources
For an enhanced learning experience, I highly recommend visiting this [complementary notes website](http://www.holehouse.org/mlclass/).
| 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. |
## Programming Exercises
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. |
## How to Contribute
We encourage contributions that enhance the repository's value. To contribute:
1. Fork the repository.
2. Create your feature branch (`git checkout -b feature/AmazingFeature`).
3. Commit your changes (`git commit -m 'Add some AmazingFeature'`).
4. Push to the branch (`git push origin feature/AmazingFeature`).
5. Open a Pull Request.
## License
This project is licensed under the [MIT License](LICENSE) - see the LICENSE file for details.