ML resources

Hi folks!

Here are some useful links for deeper understanding of ML,amd the concepts covered in the workshop.

1. Linear regression
https://medium.com/datadriveninvestor/basics-of-linear-regression-9b529aeaa0a5

2. Reinforcement learning
Tic Tac Toe example
https://towardsdatascience.com/reinforcement-learning-implement-tictactoe-189582bea542

The basics
https://deeplizard.com/learn/video/my207WNoeyA

The pros and cons
https://pythonistaplanet.com/pros-and-cons-of-reinforcement-learning/

3. KNN
https://www.datacamp.com/community/tutorials/k-nearest-neighbor-classification-scikit-learn

4. Overall example of what algorithm to use when
https://blog.statsbot.co/machine-learning-algorithms-183cc73197c

5. Neural Nets
The video for basics
https://www.youtube.com/watch?v=aircAruvnKk

Theory for Artificial neural networks
http://www.theprojectspot.com/tutorial-post/introduction-to-artificial-neural-networks-part-1/7

How to set up your first simple neural net with keras step by step
https://machinelearningmastery.com/tutorial-first-neural-network-python-keras/

Activation functions the basics, and when to use what
https://towardsdatascience.com/activation-functions-and-its-types-which-is-better-a9a5310cc8f

6. Decision trees
The basics and intuition
http://www.saedsayad.com/decision_tree.htm



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