https://github.com/samya-s/machine-learning-specialization

Materials and lab assignments for the Machine Learning Specialization by Andrew Ng
https://github.com/samya-s/machine-learning-specialization

ai artificial-intelligence classification decision-trees machine-learning ml neural-networks recommender-system regression reinforcement-learning supervised-learning unsupervised-learning

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Materials and lab assignments for the Machine Learning Specialization by Andrew Ng

• Host: GitHub
• URL: https://github.com/samya-s/machine-learning-specialization
• Owner: Samya-S
• Created: 2025-07-11T07:26:07.000Z (11 months ago)
• Default Branch: main
• Last Pushed: 2025-08-01T06:17:05.000Z (11 months ago)
• Last Synced: 2025-08-01T08:50:09.917Z (11 months ago)
• Topics: ai, artificial-intelligence, classification, decision-trees, machine-learning, ml, neural-networks, recommender-system, regression, reinforcement-learning, supervised-learning, unsupervised-learning
• Language: Jupyter Notebook
• Homepage:
• Size: 25.9 MB
• Stars: 0
• Watchers: 0
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

          
# Machine Learning Specialization

This repository contains materials and lab assignments for the [Machine Learning Specialization](https://www.coursera.org/specializations/machine-learning-introduction) by [Andrew Ng](https://www.andrewng.org/), organized into three main courses:

## Course 1: Supervised Machine Learning - Regression and Classification  

**[Go to Course 1 on Coursera](https://www.coursera.org/learn/machine-learning?specialization=machine-learning-introduction)**

- **Description:** Introduction to supervised learning, including regression and classification techniques.

- **Topics Covered:**

  - Python and Jupyter basics

  - Linear and logistic regression

  - Cost functions and gradient descent

  - Overfitting, regularization, and model evaluation

- **Conversations with Andrew (Optional)**

  - [Andrew Ng and Fei-Fei Li on Human-Centered AI](https://youtu.be/UNhC6Ox0T0o)

## Course 2: Advanced Learning Algorithms  

**[Go to Course 2 on Coursera](https://www.coursera.org/learn/advanced-learning-algorithms?specialization=machine-learning-introduction)**

- **Description:** Explores advanced machine learning algorithms and neural networks using TensorFlow and Keras.

- **Topics Covered:**

  - Neural networks and deep learning

  - Activation functions and backpropagation

  - Model evaluation and selection

  - Decision trees and ensemble methods

- **Conversations with Andrew (Optional)**

  - [Andrew Ng and Chris Manning on Natural Language Processing](https://youtu.be/6w0Po83ZmjA)

## Course 3: Unsupervised Learning, Recommenders, Reinforcement Learning  

**[Go to Course 3 on Coursera](https://www.coursera.org/learn/unsupervised-learning-recommenders-reinforcement-learning?specialization=machine-learning-introduction)**

- **Description:** Focuses on unsupervised learning, recommender systems, and reinforcement learning.

- **Topics Covered:**

  - K-means clustering and anomaly detection

  - Principal Component Analysis (PCA)

  - Collaborative and content-based recommender systems

  - Reinforcement learning fundamentals

- **Conversations with Andrew (Optional)**

  - [Andrew Ng and Chelsea Finn on AI and Robotics](https://youtu.be/IT734HriiHQ)

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## Getting Started

1. Clone this repository.

2. Open the desired course folder and navigate to the lab assignments.

3. Launch the Jupyter Notebooks to begin working through the labs.

## Requirements

- Python 3.x

- Jupyter Notebook

- Common ML libraries: numpy, matplotlib, tensorflow, keras, etc.

## License

This repository is for educational purposes only.