Machine Learning by Stanford University (Coursera)

This repository contains all the programming assignmets, quizzes, and lecture materials of the course Machine Learning taught by Andrew Ng on Coursera. After completion of this cousre you will have a intermediate level idea of some common machine learning algorithms and how it works. I will suggest to solve your assignments and quizzes on your own first but if you get stuck feel free to browse my codes and understand how it works.

Contents

• Assignments
• Lectures
• Quizzes

Programming Assignments Description

• EX - 1 : Implementing and visualizing linear regression using gradient descent as optimizer. (Accuracy)

• EX - 2 : Implementing and visualizing logistic regression using fminfunc as optimizer. (Training set accuracy: 89.0 %)

• EX - 3 : Implementing One vs All logistic regression.

• EX - 4 : Implementing a neural net with some pre trained weights.

• EX - 5 : Learning and tuning hyperparameters.

• EX - 6 : Implementing an linear6SVM.

• EX - 7 : Implementing a basic K-Means Clustering algorithm.

• EX - 8 : Learning and visualizing testing parameters for a model.

Lectures

• Lecture 1
• Lecture 2
• Lecture 3
• Lecture 4
• Lecture 5
• Lecture 6
• Lecture 7
• Lecture 8
• Lecture 9
• Lecture 10
• Lecture 11
• Lecture 12
• Lecture 13
• Lecture 14
• Lecture 15
• Lecture 16
• Lecture 17
• Lecture 18

Quizzes

• Week 1
• Week 2
• Week 3
• Week 4
• Week 5
• Week 6
• Week 7
• Week 8
• Week 9
• Week 10
• Week 11

Certificate

• Verified Certificate

References

• Machine Learning - Stanford University