Scientific Computing and Data Science Applications with Numpy, SciPy and Matplotlib
by Robert Johansson
appears in Programming and Technology.
Leverage the numerical and mathematical modules in Python and its standard library as well as popular open source numerical Python packages like NumPy, SciPy, FiPy, matplotlib and more. This fully revised edition, updated with the latest details of each package and changes to Jupyter projects, demonstrates how to numerically compute solutions and m...
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Consider Python Machine Learning by Sebastian Raschka. Recommended by 2 sources.
“Python Machine Learning pairs concise algorithm descriptions with runnable Python examples and a linked GitHub repository. Early chapters are directly practical if you follow the notebooks; later sections compress derivations and long code walkthroughs that demand slower, hands-on reading. The clearest payoff comes from adapting the examples to your own data rather than passive skimming. Expect a utilitarian finish: usable code snippets and clearer implementation patterns, but not a gentle, classroom-style sequence of exercises.”
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