Jupyter notebooks for teaching machine learning. Based on scikit-learn, with OpenML used to experiment more extensively on many datasets.
We use many code examples from the following excellent book. We urge you to read it for a more complete coverage of machine learning in Python:
"Introduction to Machine Learning with Python" by Andreas Mueller and Sarah Guido. You can find details about the book on the O'Reilly website. We'll be using the included mglearn package to make plotting easier.
Additional code examples originate from the following books (again, warmly recommended):
"Python machine learning" by Sebastian Raschka: Raschka, Sebastian. Python machine learning. Birmingham, UK: Packt Publishing, 2015..
"Python for Data Analysis" by Wes McKinney: McKinner, Wes. Python for Data Analysis. O’Reilly, 2012..
For a deeper understanding of machine learning techniques, we can recommend the following books:
"The Elements of Statistical Learning: Data Mining, Inference, and Prediction. (2nd edition)" by Trevor Hastie, Robert Tibshirani, Jerome Friedman. One of the key references of the field. Great coverage of linear models, regularization, kernel methods, model evaluation, ensembles, neural nets, unsupervised learning. The PDF is available for free.
"An Introduction to Statistical Learning (with Applications in R)" by Gareth James, Daniela Witten, Trevor Hastie and Robert Tibshirani. More introductory version of the above book, with many code examples in R. The PDF is also available for free.
"Deep Learning" by Ian Goodfellow, Yoshua Bengio, Aaron Courville. The current reference for deep learning. Chapters can be downloaded from the website.
"Gaussian Processes for Machine Learning" by Carl Edward Rasmussen and Christopher K. I. Williams. The reference for Bayesian Inference. Also see David MacKay's book for additional insights.