Machine Learning -(Course Notes & Projects)

Overview

Welcome to my repository for the Machine Learning course by Andrew Ng and also my extra learnings. This repository contains detailed notes, personal projects, and implementations of key concepts covered in the course.

Course Details

• Instructor: Andrew Ng
• Platform: Coursera / Stanford Online
• Topics Covered:
  • Supervised Learning (Linear Regression, Logistic Regression, Neural Networks)
  • Unsupervised Learning (Clustering, Dimensionality Reduction)
  • Optimization Algorithms (Gradient Descent, Backpropagation)
  • Model Evaluation and Selection
  • Regularization and Overfitting
  • Support Vector Machines and Anomaly Detection
  • Advanced ML Techniques (Deep Learning, Reinforcement Learning, Ensemble Methods)

Repository Structure

Machine-Learning-Andrew-Ng/
├── Notes/
│   ├── week1/
│   ├── week2/
│   ├── week3/
│   ├── week4/
├── Projects/
│   ├── week1/
│   ├── week2/
│   ├── week3/
│   ├── week4/
├── Datasets/
│   ├── week1/
│   ├── week2/
├── README.md

Weekly Progress

Week 1

• Topic: Introduction & Linear Regression
• 📄 Notes
• 📝 Project
• 📖 Datasets

Week 2

• Topic: Multiple Regression and Vectorization
• 📄 Notes
• 📝 Project
• 📖 Datasets

Week 3

• Topic: Multi-class Classification, Regularization, and Logistic Regression Model
• 📄 Notes
• 📝 Project

Week 4

• Topic: Neural Networks and Deep Learning
• 📄 Notes
• 📝 Project

How to Use This Repository

1. Clone the repository:

   git clone https://github.com/Greninja44/Machine-learning.git

2. Navigate to the repository directory:

   cd Machine-learning

3. Explore the notes and projects for each topic.

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Stay tuned for updates and more implementations as I progress through the course! 🚀