Python for Probability, Statistics, and Machine Learning

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.