Machine Learning

This Machine Learning course is offered by Andrew Ng from Stanford University. This repo contains all assignments (platform Octave) and not quiz's solution but have some lecture notes.

• Course: https://www.coursera.org/learn/machine-learning
• Offered by: https://www.stanford.edu/
• Instructor: https://www.andrewng.org/
• My certificate: https://www.coursera.org/account/accomplishments/records/YNAC7C5SMJ6R

Week 01:

Overview/Topics:

• Intorduction to machine learning, supervised, unsupervised learning
• Model and cost function, parameter learning: gradient descent for linear regression
• Linear Algebra review: Matrix, vector, multiplication, inverse, transpose etc.

Programming Assignments:

• [Week 01: N/A]

Week 02:

Overview/Topics:

• Environment setup for octave
• Multivariant linear regression
• Matlab/Octave basic tutorial

Programming Assignments:

• Week 02: Linear Regression

Week 03:

Overview/Topics:

• Logistic Regression: classification, hypothesis representation, decision boundary
• Cost function, Advance optimization
• Solving the problem of overfitting, Regularized linear regression

Programming Assignments:

• Week 03: Logistic Regression

Week 04:

Overview/Topics:

• Neural networks representation: Non-linear hypothesis, model representation
• and it's application

Programming Assignments:

• Week 04: Multi-class Classification and Neural Networks

Week 05:

Overview/Topics:

• Neural networks : Cost function and backpropagation: gradient checking, random initialization
• Application of neural networks: Autonomous driving

Programming Assignments:

• Week 05: Neural Network Learning

Week 06:

Overview/Topics:

• Advice for Applying Machine Learning: Evaluating a learning algorithm
• Evaluating hypothesis, model selection
• Train/validation/Test sets
• Bias vs Variance: Diagonalizing, Regularization, Learning curves
• Building a spam classifier, handling skewed data

Programming Assignments:

• Week 06: Maching Learning system design

Week 07:

Overview/Topics:

• Support Vector Machines
• Large margin in classification

Programming Assignments:

• Week 07: Support Vector Machines

Week 08:

Overview/Topics:

• Unsupervised Learning: Clustering: K-means
• Data compression , visualization
• Principle Component analysis(PCA algorithm) and applying PCA

Programming Assignments:

• Week 08: K-Means Clustering and PCA

Week 09:

Overview/Topics:

• Anomaly detection, density estimation, gausian distribution
• Learn to build an anomaly detection system
• Recommender system: Content based recommendation
• Collaborative filtering algorithm
• Low rank matrix factorization

Programming Assignments:

• Week 09: Anomaly Detection and Recommender system

Week 10:

Overview/Topics:

• Large Scale Machine Learning
• Gradient descent with large scale datasets, mini-batch gradient descent
• Stochastic gradient descent
• Online learning, map reduce and data parallelization

Programming Assignments:

• [Week 10: N/A]

Week 11:

Overview/Topics:

• Application Example: Photo OCR
• Sliding windows algorithm
• Ceilling analysis: pipeline work

Programming Assignments:

• [Week 11: N/A]

Additional Resources and Lecture notes

• Click here