This is a collection of 10 great GitHub repositories focusing on IPython, TensorFlow, Theano and related topics, for data scientists. The last one is not on GitHub.
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The 11 IPythonTutorials
- Example Machine Learning – Notebook by Randal S. Olson, supported by Jason H. Moore. University of Pennsylvania Institute for Bioinformatics
- Python Machine Learning Book – 400 pages rich in useful material just about everything you need to know to get started with machine learning … from theory to the actual code that you can directly put into action!
- Learn Data Science – The initial beta release consists of four major topics: Linear Regression, Logistic Regression, Random Forests, K-Means Clustering
- Scikit-learn Tutorial – By Jake VanderPlas, University of Washington.
- Machine Learning – This repo contains a collection of IPython notebooks detailing various machine learning algorithms. In general, the mathematics follows that presented by Dr. Andrew Ng’s Machine Learning course taught at Stanford University (materials available from ITunes U, Stanford Machine Learning), Dr. Tom Mitchell’s course at Carnegie Mellon, and Christopher M. Bishop’s “Pattern Recognition And Machine Learning”.
- Research Computing Meetup – Linux and Python for data analysis (tutorials). University of Colorado, Computational Science and Engineering.
- Theano Tutorial – A brief IPython notebook-based tutorial on basic Theano concepts, including a toy multi-layer perceptron example..
- IPython Theano Tutorials – A collection of tutorials in ipynb format that illustrate how to do various things in Theano.
- IPython Notebooks – Demonstrations and use cases for many of the most widely used “data science” Python libraries. Implementations of the exercises presented in Andrew Ng’s “Machine Learning” class on Coursera. Implementations of the assignments from Google’s Udacity course on deep learning.
- ISLR Python – This repository contains Python code for a selection of tables, figures and LAB sections from the book ‘An Introduction to Statistical Learning with Applications in R’ by James, Witten, Hastie, Tibshirani (2013).
- Graphing Data with IPython Notebook – Graphing bike path data with IPython Notebook and pandas.
For more articles about IPython for Data Science and Machine Learning, click here.
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Additional Reading
- What statisticians think about data scientists
- Data Science Compared to 16 Analytic Disciplines
- 10 types of data scientists
- 91 job interview questions for data scientists
- 50 Questions to Test True Data Science Knowledge
- 24 Uses of Statistical Modeling
- 21 data science systems used by Amazon to operate its business
- Top 20 Big Data Experts to Follow (Includes Scoring Algorithm)
- 5 Data Science Leaders Share their Predictions for 2016 and Beyond
- 50 Articles about Hadoop and Related Topics
- 10 Modern Statistical Concepts Discovered by Data Scientists
- Top data science keywords on DSC
- 4 easy steps to becoming a data scientist
- 22 tips for better data science
- How to detect spurious correlations, and how to find the real ones
- 17 short tutorials all data scientists should read (and practice)
- High versus low-level data science
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