Meta & resources

Probability, statistics and data analysis

Machine Learning: concepts & procedures

Machine Learning: fundamental algorithms

Machine Learning: model assessment

Artificial neural networks

Natural language processing

The computer science appendix

The mathematics appendix

Beautiful web of data science

Topic-specific resources are already reported in every page for details, more insight, citation purposes. Here, we list material which is comprehensively good: online courses, books, blogs, and which span several of the topics we touch on in the book. All the material listed here can be of any format.

Statistics and Probability

*C Bergstrom*and*J West,***Calling Bullshit****in the age of Big Data**, a course on spotting manipulative use of data, wrong conclusions and overall misunderstandings*D Huff*,**How to lie with Statistics**, a lovely little book on the common mistakes and misunderstandings around the use of numbers for reaching conclusions. Old (1954), but very valuable and entertaining*T Vigen*,**Spurious Correlations**, a project which visually displays improbable correlations between sets of data to demonstrate that, well, correlation is not causation

Machine Learning - general material

Neural Networks

*F Chollet*,**Deep Learning with Python**, Manning, 2017, a book by the creator of Keras- The
**TensorFlow Neural Network playground**, an interactive tool to visualise the inner workings of ANNs

Computer Vision

**â€‹****The Hypermedia Image Processing Reference**, a website built by the University of Edinburgh, School of Informatics- â€‹
**Pyimagesearch**, a website curated by A Rosebrock on Computer Vision and Machine/Deep Learning on images, with tutorials for OpenCV and lots of good material

Coding and Computer Science

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**Practical Business Python**, a website By C Moffitt devoted to best practices on using Python for practical reasons, it's very good *Gayle Laakmann McDowell*,**Cracking the Coding Interview**, CareerCup

Python references relevant to Data Science

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**Scipy lecture notes**- they're pretty brilliant and obviously focussed on Python, but you can learn lots of concepts in data - â€‹
**scikit-learn**has tutorials and extensive explanations for every algorithm they support, as well as general notes on Machine Learning **â€‹****The Hitchhiker's guide to Python**(not particularly targeted at data science, but a very useful reference)

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