Reflecting on DS, ML
[Book]
Weapons of Math Destruction: How Big Data Increases Inequality and Threatens Democracy (2016), Cathy O'Neil.
[Book]
The Truthful Art: Data, Charts, and Maps for Communication (2016), Alberto Cairo.
[Web]
The Foundations of Algorithmic Bias (2016), Zachary C. Lipton.
[Book]
The Signal and the Noise: Why So Many Predictions Fail - but Some Don't (2012), Nate Silver.
DS, ML in Practice
[Book]
Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems (2019, 2nd edition), Aurélien Géron. See also the ageron/handson-ml Github repository supporting the book.
[Web]
Comparative Study on Classic Machine learning Algorithms: Quick summary on various ML algorithms (2018), Danny Varghese.
[Book]
Python for Data Analysis: Data Wrangling with Pandas, NumPy, and IPython (2017, 2nd edition), Wes McKinney.
[Web]
The Treachery of Leakage (2016), Colin Fraser.
[Web]
Deep Learning in a Nutshell: Core Concepts (2015), Tim Dettmers.
[Web]
The Unreasonable Effectiveness of Recurrent Neural Networks (2015), Andrej Karpathy.
[Web]
Leakage in Data Mining: Formulation, Detection, and Avoidance (2011), Shachar Kaufman, Saharon Rosset, Claudia Perlich.