Top 10 IPython Notebook Tutorials for Data Science and Machine Learning

@machinelearnbot

This is a great project undertaken by Jordi Warmenhoven to implement the concepts from the book An Introduction to Statistical Learning with Applications in R by James, Witten, Hastie, Tibshirani (2013) in Python (the book has practical exercises in R, as you may have guessed). The book is freely available in as a PDF, which makes this repo even more attractive to those looking to learn.


Top 10 IPython Notebook Tutorials for Data Science and Machine Learning

#artificialintelligence

This post is made up of a collection of 10 Github repositories consisting in part, or in whole, of IPython (Jupyter) Notebooks, focused on transferring data science and machine learning concepts. This warmup notebook is from postdoctoral researcher Randal Olson, who uses the common Python ecosystem data analysis/machine learning/data science stack to work with the Iris dataset. Aaron Masino has shared a series of very detailed, very technical machine learning IPython Notebook learning resources. From UC Boulder's Research Computing group, this older collection of notebooks (it's from way back in Fall 2013) covers a wide range of material, with an apparent focus on Linux command line-powered data management.


Top 10 IPython Notebook Tutorials for Data Science and Machine Learning

#artificialintelligence

This is a great project undertaken by Jordi Warmenhoven to implement the concepts from the book An Introduction to Statistical Learning with Applications in R by James, Witten, Hastie, Tibshirani (2013) in Python (the book has practical exercises in R, as you may have guessed). The book is freely available in as a PDF, which makes this repo even more attractive to those looking to learn.


More Machine Learning for Hackers

#artificialintelligence

The commonest questions about data science are to do with getting started. I have even found myself vacillating between these two states -- and to limit the sense of panic and doom that normally accompanies this statelessness, I maintain a list of resources that contain instructional material which achieves a healthy tradeoff between the two states. In other words, these are resources that help you learn machine learning and its underlying disciplines, as well as the nuances of the software tools available to implement them. On top of my list is Machine Learning for Hackers. One of the reasons I love this book is that it is actually a textbook on machine learning meant for hackers.


The Guide to Learning Python for Data Science

@machinelearnbot

Another essential skill in data analysis is data . Visuals are extremely important for both exploratory data analysis, as well the communication of your results. Matplotlib is the most commonly used library for this in Python. Get inspired by viewing some plots and graphs: Matplotlib Gallery Take a look at some sample code: Matplotlib Examples Review the Matplotlib chapter on DataCamp: DataCamp Python for Data Science Come up with some visualizations for your toy dataset.