This is the notebook for Machine Learnig Coursera online course, instructed by Prof. Andrew Ng.
1.1 Cost Function and Gradient Descent
3.1 Logistic Regression
3.2 Multiclass Classification
5.1 Cost Function
5.2 Back propagation
6.1 Bias vs. Variance
6.2 Learning Curve
7.1 Confusion Matrix
7.2 Precision vs. Recall
8.1 ReLU
8.2 Math of SVM
8.3 Kernel SVM
10.1 PCA
10.2 LDA (see another repository: Machine learning and Deep learning A-Z)
11.1 Gaussian Distribution(Normal Distribution)
11.2 Density Estimation
11.3 Multivariate Gaussian Distribution
11.4 Anomaly detection with multiple Gaussian distribution
13.1 Stochastic Gradient Decent
13.2 Mini-batch Gradient Decent
13.3 Stochastic Gradient Decent Convergence
13.4 Advanced Topics of Large Scale Dataset
13.4.1 Online Learning(reinforcement learning) (see another repository: Machine learning and Deep learning A-Z)
14.1 OCR pipeline
14.2 Sliding window
14.3 Ceiling Analysis