Machine-Learning-By-Andrew-NG

Machine Learning by Stanford University Taught by Andrew NG.

The Code done by using Octave.

You may use either MATLAB or Octave (>= 3.8.0).

The solutions of the execrcies of the Course.

Exercise .1

• Linear Regression with one and Multi variable.

• Computing the Cost function.

• Gradient Descent.

• Feature Normalization.

• Visualisation.

Exercise .2

• Compute the cost and Gradient for Logistic regression
• Apply Regularization for the Cost and the Graident for Logistic regression

Excercise .3

• Neural Network
• Cost and Gradient for Neural Network
• Multi-class classifier using one-vs-all methods

Excercise .4

• Apply the backpropagation
• Add regularized term

Excercise .5

• Regularized linear regression cost function
• Generates a learning curve
• Generates a cross validation curve

Excercise .6

• Work with SVM
• Gaussian kernel for SVM
• Parameters to use for Dataset 3

Excercise .7

• Work K-mean algorithm
• Perform principal component analysis
• Projects a data set into a lower dimensional space
• Recovers the original data from the projection
• Find closest centroids (used in K-means)
• Compute centroid means (used in K-means)
• Initialization for K-means centroids

Excercise .8

• Work with Anomaly Detection and RecommenderSystems
• Estimate the parameters of a Gaussian distribution with a diagonal covariance matrix
• Find a threshold for anomaly detection
• Implement the cost function for collaborative filtering