🧠 Machine Learning Algorithms — From Scratch & Using Libraries

This repository contains implementations of core Machine Learning algorithms, both:

• ✅ From Scratch (with Python/NumPy)
• ⚙️ Using scikit-learn and other libraries

It is inspired by my journey through the legendary CS229: Machine Learning course by Prof. Andrew Ng at Stanford University.

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📚 Algorithms Implemented

🔧 From Scratch (Manual Implementation)

• Linear Regression (Batch Gradient Descent & Normal Equation)
• Logistic Regression
• Gaussian Discriminant Analysis (GDA)
• Naive Bayes (Gaussian)
• Mean Absolute Error (MAE) calculation
• More coming soon...

⚙️ With Prebuilt Libraries

• Scikit-learn implementations of:
  • Linear & Logistic Regression
  • Naive Bayes
  • Support Vector Machines (SVM)
  • Performance metrics (MAE, RMSE, R², etc.)
• Jupyter notebooks for experimentation and visualization

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🛠️ Tools Used

• Python
• NumPy
• Scikit-learn
• Pandas
• Matplotlib / Seaborn (for visualizations)
• Jupyter Notebooks

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🧑‍🏫 Credits

This work is heavily inspired by:

• CS229: Machine Learning, Stanford University
  📌 https://cs229.stanford.edu/
• Lectures by Prof. Andrew Ng
  📌 https://www.andrewng.org/

A sincere thanks to the CS229 team for the clarity and depth of their teaching materials.

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🚧 Work in Progress

More models and projects will be added as I progress through the CS229 curriculum and beyond.

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🤝 Contributions

Feel free to open issues, suggest improvements, or fork the repo if you'd like to build on this work.

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📜 License

This repository is open-sourced under the MIT License.