Sowmik23/Machine_Learning

This Machine Learning course is offered by Andrew Ng from Stanford University. This repo contains all assignments (platform Octave) and quiz's solution.

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