Scipy Cookbook – http://scipy-cookbook.readthedocs.io/items/robust_regression.html
Packet book – Data mining with python – https://github.com/PacktPublishing/Learning-Data-Mining-with-Python-Second-Edition
Introduction To Data Mining book resources – https://www-users.cs.umn.edu/~kumar001/dmbook/index.php
Course: Knowledge Discovery and Data Mining – http://www.cse.ust.hk/~leichen/courses/comp5331/
Statistical Data Mining Tutorials – https://www.autonlab.org/tutorials/list.html
Math is fun – https://www.mathsisfun.com/data/index.html
simple Logistic regression – https://www.kaggle.com/emilyhorsman/basic-logistic-regression-with-numpy
regression with 2 dimensions – http://faculty.cas.usf.edu/mbrannick/regression/Reg2IV.html
course: Introduction to ML – http://courses.washington.edu/css581/CSS%20581%20-%20Introduction%20to%20Machine%20Learning.html
Great ML site – http://mnemstudio.org/artificial-intelligence-introduction.htm
SOM – http://davis.wpi.edu/~matt/courses/soms/index.html#Main%20Algo
Course: Machine Learning – http://cs229.stanford.edu/syllabus.html
course – ML – http://ciml.info
Neural Networks
http://www.bogotobogo.com/python/scikit-learn/Artificial-Neural-Network-ANN-1-Introduction.php
https://github.com/Einsteinish/Artificial-Neural-Networks-with-Jupyter
https://www.nnwj.de/backpropagation.html
https://mashimo.wordpress.com/2015/09/13/back-propagation-for-neural-network/
http://iamtrask.github.io/2015/07/12/basic-python-network/
https://www.kdnuggets.com/2017/10/tensorflow-building-feed-forward-neural-networks-step-by-step.html
http://briandolhansky.com/blog/artificial-neural-networks-linear-regression-part-1
Book – http://neuralnetworksanddeeplearning.com/chap1.html
Book Examples – https://github.com/mnielsen/neural-networks-and-deep-learning
NPL
https://courses.engr.illinois.edu/cs546/sp2018/
https://courses.engr.illinois.edu/cs447/fa2017/syllabus.html