This project for learning Machine Learning as per taught by Professor Thomas Trapenberg at DAL.

There are 7 Assignments covered in this course :-

Assignment 1– Introduction to Python for Machine Learning

Assignment 2– Introduction to sklearn for Machine Learning

Assignment 3– Regression, Probability Theory, and Optimization

Assignment 4– Probabilistic regression and classification,Generative Models, and Bayes Nets

Assignment 5– Multilayer Perceptron

Assignment 6– Convolutional Neural Networks and MNIST with Tensorflow

Assignment 7– Reinforcement learning

In this project I have tried to provide all the solutions to these assignments, and the best part and the most interesting part is to implement the ML algorithms from scratch.

Will keep updating this project with more and better refined solutions.