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.

• Assignments
• Lectures
• Quizzes

• 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.

• 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

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

• Verified Certificate

• Machine Learning - Stanford University