🎓 Supervised Machine Learning: Regression and Classification (Andrew Ng)

   

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📘 About the Course

This repository contains my work and notes from the first course of the Machine Learning Specialization by DeepLearning.AI and Stanford Online, taught by Andrew Ng.

🔍 Course Name:

Supervised Machine Learning: Regression and Classification

🧑‍🏫 Instructors:

• Andrew Ng – Founder of DeepLearning.AI, Co-founder of Coursera
• Aarti Bagul – Curriculum Engineer
• Geoff Ladwig – Curriculum Engineer
• Eddy Shyu – Instructor, DeepLearning.AI

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🎯 What You Will Learn

• Implement machine learning models using Python, NumPy, and scikit-learn
• Build and train linear regression and logistic regression models
• Understand the difference between regression and classification
• Handle multiple input features, feature scaling, and feature engineering
• Use gradient descent to train models
• Prevent overfitting using regularization

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🧠 Course Structure

Week	Topics Covered
Week 1	Introduction to ML, Supervised vs Unsupervised Learning, Linear Regression, Gradient Descent
Week 2	Multiple Features, Vectorization, Feature Scaling, Polynomial Regression, scikit-learn
Week 3	Classification with Logistic Regression, Cost Function for Classification, Overfitting & Regularization

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📂 Repository Structure

ml-supervised-learning/
├── MODULE1/                      # Week 1 – Linear Regression with One Variable
│   ├── notebooks/
│   ├── quiz\_screenshots/
│   └── README.md
├── MODULE2/                      # Week 2 – Multiple Variables and Feature Engineering
│   ├── notebooks/
│   ├── quiz\_screenshots/
│   └── README.md
├── MODULE3/                      # Week 3 – Classification and Regularization
│   ├── notebooks/
│   ├── quiz\_screenshots/
│   └── README.md
└── README.md                     # This file

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🧾 Course Details

• Platform: Coursera

• Level: Beginner

• Duration: ~3 weeks (5 hours/week)

• Languages Available:
  English + Subtitles in Arabic, French, Hindi, Spanish, Chinese (Simplified), and more

• Part of:
  🧠 Machine Learning Specialization – Course 1 of 3

• Certificate Earned:

• Graded Assignments: ✅ Completed (100%)

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🧑‍💻 Technologies Used

• Python 🐍
• NumPy
• scikit-learn
• Jupyter Notebook

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🏁 Final Outcome

By the end of this course, I gained a strong foundation in supervised machine learning and built practical implementations of:

• Linear regression (single and multiple features)
• Logistic regression for binary classification
• Gradient descent optimizations
• Feature scaling and polynomial regression
• Regularization to mitigate overfitting

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📜 License

Content in this repository is for educational purposes only and is subject to Coursera Honor Code and the Creative Commons CC BY-NC-SA 4.0 License.

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