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