Links

First things first,

• A list of background music. Link.

• Sketching Link

Data Science

• Top 10 DS courses # http://bigdata-madesimple.com/review-of-top-10-online-data-science-courses/

• https://www.kaggle.com/wiki/Tutorials

• https://github.com/ujjwalkarn/Machine-Learning-Tutorials

• http://www.kdnuggets.com/2015/06/top-20-python-machine-learning-open-source-projects.html

• http://brettromero.com/wordpress/data-science-a-kaggle-walkthrough-introduction/ Kaggle

• https://github.com/jmschrei/pomegranate Pomegrante

Git

• Git: How to set up remote git branch # http://www.gitguys.com/topics/adding-and-removing-remote-branches/

Python

• https://pymotw.com/3/

Statistics

• http://stattrek.com/online-calculator/bayes-rule-calculator.aspx

Quick Short Cuts

• https://image-store.slidesharecdn.com/0532ed40-2144-4f78-a769-3f3249044806-original.png

Ipython Notes for learning

• https://my.datascientistworkbench.com/tools/jupyter-notebook/

Lots of quick & interesting slides

• https://speakerdeck.com/jakevdp Statistics 4 Hackers

• https://www.youtube.com/watch?v=nCPf8zDJ0d0 Introduction of Deep Learning.

Data Scientist Workbench:

It’s a free all-in-one solution for people interested in performing data analysis. The Data Scientist Workbench includes:

• OpenRefine to clean up messy data.

• Jupyter notebooks supporting Python, R, and Scala (with access to Apache Spark for Big Data processing).

• Apache Zeppelin notebooks.

• RStudio in your browser.

https://my.datascientistworkbench.com/

QuickSlides on NLTP - Natural Language Text Processing

• https://www.cse.iitb.ac.in/~neelamadhavg09/docs/dependency_parsing.pdf # Articles on semantic text-parsing - dependency parsing.

Kaggle Tips:

• http://blog.kaggle.com/2016/07/21/approaching-almost-any-machine-learning-problem-abhishek-thakur/

Related reading:

Part 1 of this blog post series: Orientation

• http://blog.turi.com/how-to-evaluate-machine-learning-models-part-2a-classification-metrics

Part 2b: Ranking and regression metrics

Part 3: Validation and offline testing

Part 4: Hyperparameter tuning

Part 5: A/B testing

Tom Fawcett’s 2006 Pattern Recognition Letters paper on An Introduction to ROC Analysis.

Chapter 7 of Data Science for Business discusses the use of Expected Value as a useful classification metric, especially in cases of skewed data sets.

Research Articles

• Do we Need Hundreds of Classifiers to Solve Real World Classification Problems? Evaluate 179 classifiers arising from 17 families

• A Data Complexity Analysis of Comparative Advantages of Decision Forest Constructors

• https://www.r-bloggers.com/a-brief-tour-of-the-trees-and-forests/

• https://en.wikipedia.org/wiki/Machine_learning

Note: This post was updated on April 16, 2015. Thanks to @aatallah for demystifying the origin of the name “ROC curve,” and to Joe McCarthy for the helpful references.

PDF/Slides Generator for presentations

• http://www.tug.org/mactex/ # This is mac version of LaTeX. There is an extension called Beamer to try.