Python for Probability, Statistics, and Machine Learning

Authors: Unpingco, José
Explains how to simulate, conceptualize, and visualize random statistical processes and apply machine learning methods
Connects to key open-source Python communities and corresponding modules focused on the latest developments in this area
Outlines probability, statistics, and machine learning concepts using an intuitive visual approach, backed up with corresponding visualization codes

This book covers the key ideas that link probability, statistics, and machine learning illustrated using Python modules in these areas. The entire text, including all the figures and numerical results, is reproducible using the Python codes and their associated Jupyter/IPython notebooks, which are provided as supplementary downloads. The author develops key intuitions in machine learning by working meaningful examples using multiple analytical methods and Python codes, thereby connecting theoretical concepts to concrete implementations. Modern Python modules like Pandas, Sympy, and Scikit-learn are applied to simulate and visualize important machine learning concepts like the bias/variance trade-off, cross-validation, and regularization. Many abstract mathematical ideas, such as convergence in probability theory, are developed and illustrated with numerical examples. This book is suitable for anyone with an undergraduate-level exposure to probability, statistics, or machine learning and with rudimentary knowledge of Python programming.

Number of Illustrations and Tables
103 b/w illustrations, 7 illustrations in colour
Topics
Communications Engineering, Networks
Appl. Mathematics / Computational Methods of Engineering
Statistics for Engineering, Physics, Computer Science, Chemistry and Earth Sciences
Probability and Statistics in Computer Science
Data Mining and Knowledge Discovery

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