This repository contains the course materials and assignments done as part of the Machine learning course by Andrew Ng.
- What is Machine learning?
- Supervised Learning
- Unsupervised Learning
- Linear Regression with One Variable
- Linear Algebra Review
- Linear Regression with Multiple Variables
- Multivariate Linear Regression - Gradient Descent
- Multivariate Linear Regression - Normal equation
- Octave tutorial
- Logistic Regression
- Classification and Representation
- Logistic Regression Model
- Multiclass Classification
- Regularization
- Solving the Problem of Overfitting
- Neural Networks: Representation
- Motivations
- Neural Networks
- Applications
- Neural Networks: Learning
- Cost Function and Backpropagation
- Backpropagation in Practice
- Application of Neural Networks - Autonomous driving
- Advice for applying machine learning
- Evaluating a Learning Algorithm
- Bias vs. Variance
- Machine learning system design
- Building a Spam Classifier
- Handling Skewed Data
- Using Large Data Sets
- Support Vector Machines
- Large Margin Classification
- Kernels
- SVMs in Practice
- Unsupervised Learning
- Clustering
- Dimensionality Reduction
- Principal Component Analysis
- Applying PCA