Machine Learning Specialization 📊🤖

This repository contains my completed assignments, labs, and projects from the Machine Learning Specialization offered by Andrew Ng and DeepLearning.AI on Coursera. It provides a practical, Python-based introduction to foundational machine learning algorithms and techniques.

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🧠 Overview

This specialization consists of three courses, each focusing on a core aspect of Machine Learning:

1. Supervised Machine Learning: Regression and Classification
2. Advanced Learning Algorithms
3. Unsupervised Learning, Recommenders, Reinforcement Learning

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📁 Contents

📌 Course 1: Supervised Machine Learning

• Linear Regression with one and multiple variables
• Gradient Descent
• Logistic Regression for classification tasks
• Evaluation metrics (Precision, Recall, F1-score)

📌 Course 2: Advanced Learning Algorithms

• Neural Networks from scratch (Forward & Backward Propagation)
• Vectorized implementations
• Regularization techniques (L2, dropout)
• Decision Trees, Random Forests, and Boosting
• Support Vector Machines (SVMs)

📌 Course 3: Unsupervised Learning and More

• K-means Clustering
• Principal Component Analysis (PCA)
• Anomaly Detection
• Recommender Systems
• Introduction to Reinforcement Learning and Q-learning

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📚 References

Machine Learning Specialization on Coursera Taught by Andrew Ng