From fce2231b81d46900a50abaec161b20a2b63909c9 Mon Sep 17 00:00:00 2001 From: Kevin Markham Date: Thu, 27 Oct 2016 22:21:20 -0400 Subject: [PATCH] Add resources from Data School --- README.md | 6 +++++- 1 file changed, 5 insertions(+), 1 deletion(-) diff --git a/README.md b/README.md index 152908f..fd72684 100644 --- a/README.md +++ b/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)