Machine_Learning_Coursera

Code for coursera machine learning programming assignment.

Assignment 1: Linear Regression[Week2]

Part	Name	Score
1	Warm up exercise	10 / 10
2	Compute cost for one variable	40 / 40
3	Gradient descent for one variable	50 / 50
4	Feature normalization	0 / 0
5	Compute cost for multiple variables	0 / 0
6	Gradient descent for multiple variables	0 / 0
7	Normal equations	0 / 0

Assignment 2: Logistic Regression[Week3]

Part	Name	Score
1	Sigmoid function	5 / 5
2	Compute cost for logistic regression	30 / 30
3	Gradient for logistic regression	30 / 30
4	Predict function	5 / 5
5	Compute cost for regularized LR	15 / 15
6	Gradient for regularized LR	15 / 15

Assignment 3: Multi-class Classification and Neural Networks[Week4]

Part	Name	Score
1	Regularized logistic regression	30 / 30
2	One-vs-all classifier training	20 / 20
3	One-vs-all classifier prediction	20 / 20
4	Neural network prediction function	30 / 30

Assignment 4: Neural Network Learning[Week5]

Part	Name	Score
1	Feedforward and cost function	30 / 30
2	Regularized cost function	15 / 15
3	Sigmoid gradient	5 / 5
4	Neural net gradient function (backpropagation)	40 / 40
5	Regularized gradient	10 / 10

Assignment 5: Regularized Linear Regression and Bias/Variance[Week6]

Part	Name	Score
1	Regularized linear regression cost function	25 / 25
2	Regularized linear regression gradient	25 / 25
3	Learning curve	20 / 20
4	Polynomial feature mapping	10 / 10
5	Cross validation curve	20 / 20

Assignment 6: Support Vector Machines[Week7]

Part	Name	Score
1	Gaussian kernel	25 / 25
2	Parameters (C, sigma) for dataset 3	25 / 25
3	Email preprocessing	25 / 25
4	Email feature extraction	25 / 25

Assignment 7: K-Means Clustering and PCA[Week8]

Part	Name	Score
1	Find closest centroids	30 / 30
2	Compute centroid means	30 / 30
3	PCA	20 / 20
4	Project data	10 / 10
5	Recover data	10 / 10

Assignment 8: Anomaly Detection and Recommender Systems[Week9]

Part	Name	Score
1	Estimate gaussian parameters	15 / 15
2	Select threshold	15 / 15
3	Collaborative filtering cost	20 / 20
4	Collaborative filtering gradient	30 / 30
5	Regularized cost	10 / 10
6	Gradient with regularization	10 / 10