Machine Learning A-Z™: Hands-On Python & R In Data Science
by Kirill Eremenko and Hadelin de Ponteves on Udemy

Actually a couple of new jobs and task at work and obviously major curiosity have led me to have a dive into Machine Learning. Guess what, I absolutely love it. It’s fantastic to get your head around the theory, but also try out some simple examples of applied ML – even if it is just to understand when people talk about it. Actually applying it to own real world problems is of course a different story, but this is a first step for me. It all boils down to ask the right question and to understand what the data might be able to tell you, rather then using Machine Learning for the sake of it.
I highly recommend the course, join me if you’d like!

This is the table of content as described on Udemy:

Part 1 – Data Preprocessing
Part 2 – Regression:

Simple Linear Regression

Multiple Linear Regression

Polynomial RegressionSVR

Decision Tree Regression

Random Forest Regression

Part 3 – Classification:

Logistic Regression

K-NN

SVM

Kernel SVM

Naive Bayes

Decision Tree Clssification

Random Forest Classification

Part 4 – Clustering:

K-Means

Hierarchical Clustering

Part 5 – Association Rule Learning:

Apriori

Eclat

Part 6 – Reinforcement Learning:

Upper Confidence Bound

Thompson Sampling

Part 7 – Natural Language Processing: Bag-of-words model and algorithms for NLP
Part 8 – Deep Learning: