Machine Learning by Stanford University

Week 1 - Introduction
	 Linear Regression with One Variable
	 Linear ALgebra Review
Week 2 - Linear Regression with Multiple Variables
	 Octave/Matlab Tutorial
Week 3 - Classification
	 Logistic Regression Model
	 Multiclass Classification
	 Solving Overfitting
	 Regularized Linear Regression
	 Regularized Logistic Regression
Week 4 - Neural Network
	 Applications
Week 5 - Neural Network Implementation
	 Cost Function and Backpropagation
	 Forward Propagation
	 Random Initialization
Week 6 - Evaluation of a Learning Algorithm
	 Model Selection
	 Bias/Variance
	 Debugging a Learning Algorithm
Week 7 - Support Vector Machine
	 Linear Kernel
	 Gaussian Kernel
Week 8 - Clustering Algorithms
	 K-means
	 Principal Component Analysis(PCA)
Week 9 - Anomaly Detection
	 Multivariate Gaussian Distribution
	 Recommender System
Week 10- Stochastic Gradient Descent
	 Bacth Gradient Descent
	 Mini-batch Gradient Descent
	 Map Reduce
Week 11- Optical Character Recognition(OCR)
	 Sliding Window