Classical ML with Python

CPSC392

Efficient Python Tricks and Tools for Data Scientists — Effective Python for Data Scientists

Neural Networks

Assignments and Exercises : Up to Exercise 5

https://github.com/dibgerge/ml-coursera-python-assignments

Lecture 1.1 - What Is Machine Learning - [ Machine Learning | Andrew Ng ]

Lecture 1.2 - Supervised Learning - [ Machine Learning | Andrew Ng ]

Lecture 6.1 - Logistic Regression | Classification - - [ Machine Learning | Andrew Ng]

Lecture 6.2 - Logistic Regression | Hypothesis Representation - [ Machine Learning | Andrew Ng]

Lecture 6.3 - Logistic Regression | Decision Boundary - [ Machine Learning | Andrew Ng]

Lecture 6.4 - Logistic Regression | Cost Function - [ Machine Learning | Andrew Ng]

Lecture 6.5 - Logistic Regression | Simplified Cost Function And Gradient Descent - [ Andrew Ng]

Lecture 6.6 - Logistic Regression | Advanced Optimization - [ Machine Learning | Andrew Ng]

Lecture 6.7 - Logistic Regression | MultiClass Classification OneVsAll - [Andrew Ng]

Lecture 7.1 - Regularization | The Problem Of Overfitting - [ Machine Learning | Andrew Ng]

Lecture 7.4 - Regularization | Regularized Logistic Regression - [ Machine Learning | Andrew Ng]

Lecture 8.1 - Neural Networks Representation | Non Linear Hypotheses - [Andrew Ng]