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.