| Python for Probability, Statistics, and Machine Learning (Springer) |
| Monday, 22 July 2019 | |||
This book, fully updated for Python version 3.6+, covers the key ideas that link probability, statistics, and machine learning illustrated using Python modules in these areas. Author Dr. José Unpingco develops key intuitions in machine learning by working meaningful examples using multiple analytical methods and Python codes. Detailed proofs for certain important results are also provided. Modern Python modules like Pandas, Sympy, Scikit-learn, Tensorflow, and Keras are applied to simulate and visualize important machine learning concepts like the bias/variance trade-off, cross-validation, and regularization. Author: Dr. José Unpingco
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