Add resources from Data School

README.md

@@ -137,6 +137,7 @@ Each day I take one subject from the list below, read it cover to cover, take no
 - [ ] [Machine Learning basics for a newbie](https://www.analyticsvidhya.com/blog/2015/06/machine-learning-basics/)
 - [ ] [How do you explain Machine Learning and Data Mining to non Computer Science people?](https://www.quora.com/How-do-you-explain-Machine-Learning-and-Data-Mining-to-non-Computer-Science-people)
 - [ ] [Machine Learning: Under the hood. Blog post explains the principles of machine learning in layman terms. Simple and clear](https://georgemdallas.wordpress.com/2013/06/11/big-data-data-mining-and-machine-learning-under-the-hood/)
+- [ ] [What is machine learning, and how does it work?](https://www.youtube.com/watch?v=elojMnjn4kk&list=PL5-da3qGB5ICeMbQuqbbCOQWcS6OYBr5A&index=1)
 
 ## Machine learning mastery
 - [ ] [The Machine Learning Mastery Method](http://machinelearningmastery.com/machine-learning-mastery-method/)
@@ -177,6 +178,7 @@ Each day I take one subject from the list below, read it cover to cover, take no
 - [ ] [Top 10 data mining algorithms in plain English](https://rayli.net/blog/data/top-10-data-mining-algorithms-in-plain-english/)
 - [ ] [10 Machine Learning Terms Explained in Simple English](http://blog.aylien.com/10-machine-learning-terms-explained-in-simple/)
 - [ ] [A Tour of Machine Learning Algorithms](http://machinelearningmastery.com/a-tour-of-machine-learning-algorithms/)
+- [ ] [Comparing supervised learning algorithms](http://www.dataschool.io/comparing-supervised-learning-algorithms/)
 
 ## Beginner Books
 - [ ] [Data Smart: Using Data Science to Transform Information into Insight 1st Edition](https://www.amazon.com/Data-Smart-Science-Transform-Information/dp/111866146X)
@@ -206,6 +208,7 @@ Each day I take one subject from the list below, read it cover to cover, take no
 - [ ] [An Introduction to Statistical Learning](http://www-bcf.usc.edu/~gareth/ISL/)
 	- [GitHub repository(R)](http://www-bcf.usc.edu/~gareth/ISL/code.html)
 	- [GitHub repository(Python)](https://github.com/JWarmenhoven/ISLR-python)
+	- [Videos](http://www.dataschool.io/15-hours-of-expert-machine-learning-videos/)
 - [ ] [Building Machine Learning Systems with Python](https://www.packtpub.com/big-data-and-business-intelligence/building-machine-learning-systems-python)
 	- [GitHub repository](https://github.com/luispedro/BuildingMachineLearningSystemsWithPython)
 - [ ] [Probabilistic Programming & Bayesian Methods for Hackers](https://camdavidsonpilon.github.io/Probabilistic-Programming-and-Bayesian-Methods-for-Hackers/)
@@ -226,7 +229,8 @@ Each day I take one subject from the list below, read it cover to cover, take no
 	- [Video](https://www.youtube.com/watch?list=PL1bKyu9GtNYHcjGa6ulrvRVcm1lAB8he3&v=W62ehrnOVqo)
 	- [Resources](https://bigml.com/releases)
 - [ ] [mathematicalmonk's Machine Learning tutorials](https://www.youtube.com/playlist?list=PLD0F06AA0D2E8FFBA)
-
+- [ ] [Machine learning in Python with scikit-learn](https://www.youtube.com/playlist?list=PL5-da3qGB5ICeMbQuqbbCOQWcS6OYBr5A)
+	- [GitHub repository](https://github.com/justmarkham/scikit-learn-videos)
 
 ## MOOC
 - [ ] [Udacity’s Intro to Machine Learning](https://www.udacity.com/course/intro-to-machine-learning--ud120)