https://www.coursera.org/learn/python-machine-learning
https://archive.ics.uci.edu/ml/index.php
This Nov. 2016 article by Zachary C. Lipton from the blog Approximately Correct discusses why and how automated processes for decision-making, particularly applications of machine learning, can exhibit bias in subtle and not-so-subtle ways. It's self-contained and includes a mini-review of machine learning concepts that reinforces what's covered in Module 1. This reading is optional for completion of the course.
http://approximatelycorrect.com/2016/11/07/the-foundations-of-algorithmic-bias/
If you're interested in the more general topic of ethics in data science, we recommend this online course in Data Science Ethics by Prof. H.V. Jagadish of the University of Michigan.
https://www.edx.org/course/data-science-ethics-michiganx-ds101x-1
A Few Useful Things to Know about Machine Learning
https://homes.cs.washington.edu/~pedrod/papers/cacm12.pdf
Genetic Test for Autism Refuted
http://www.the-scientist.com/?articles.view/articleNo/38030/title/Genetic-Test-for-Autism-Refuted/
Control Groups in Real Life
https://ai.stanford.edu/~ronnyk/2007GuideControlledExperiments.pdf
NNs made easy
https://techcrunch.com/2017/04/13/neural-networks-made-easy/
TensorFlow NN playground
http://playground.tensorflow.org
Deep Learning in a Nutshell: Core Concepts https://devblogs.nvidia.com/parallelforall/deep-learning-nutshell-core-concepts/
Assisting Pathologists in Detecting Cancer with Deep Learning https://research.googleblog.com/2017/03/assisting-pathologists-in-detecting.html
Data Leakage:
- https://medium.com/@colin.fraser/the-treachery-of-leakage-56a2d7c4e931
- http://www.cs.umb.edu/~ding/history/470_670_fall_2011/papers/cs670_Tran_PreferredPaper_LeakingInDataMining.pdf
- https://www.kaggle.com/c/the-icml-2013-whale-challenge-right-whale-redux/discussion/4865#25839#post25839
- http://martin.zinkevich.org/rules_of_ml/rules_of_ml.pdf
Unsupervised ML: