Machine Learning Specialization - Andrew Ng

This repository contains the coursework and assignments for the Machine Learning Specialization by Andrew Ng. The specialization is divided into three main courses, each with multiple weeks of content. All work is organized by course and week, with corresponding Jupyter notebooks and other resources.

Courses Overview

1. Supervised Machine Learning

• Week 1: Introduction to supervised learning, linear regression, cost function, and gradient descent.
• Week 2: Advanced linear regression techniques, feature engineering, and polynomial regression.
• Week 3: Logistic regression, classification, and regularization.

2. Advanced Learning Algorithms

• Week 1: Neural networks and deep learning basics.
• Week 2: Advanced neural network techniques, including regularization and optimization.
• Week 3: Model evaluation, selection, and diagnosing bias/variance.
• Week 4: Decision trees and ensemble methods.

3. Unsupervised Learning, Recommenders, Reinforcement Learning

• Week 1: Introduction to unsupervised learning, anomaly detection, and K-means clustering.
• Week 2: Recommender systems and principal component analysis (PCA).
• Week 3: Basics of reinforcement learning and implementation of simple models.