Python for Probability, Statistics, and Machine Learning

by José Unpingco

Using a novel integration of mathematics and Python codes, this book illustrates the fundamental concepts that link probability, statistics, and machine learning, so that the reader can not only employ statistical and machine learning models using modern Python modules, but also understand their relative strengths and weaknesses. To clearly connect theoretical concepts to practical implementations, the author provides many worked-out examples along with "Programming Tips" that encourage the reader to write quality Python code. The entire text, including all the figures and numerical results, is reproducible using the Python codes provided, thus enabling readers to follow along by experimenting with the same code on their own computers. Modern Python modules like Pandas, Sympy, Scikit-learn, Statsmodels, Scipy, Xarray, Tensorflow, and Keras are used to implement and visualize important machine learning concepts like the bias/variance trade-off, cross-validation, interpretability, and regularization. Many abstract mathematical ideas, such as modes of convergence in probability, are explained and illustrated with concrete numerical examples. This book is suitable for anyone with undergraduate-level experience with probability, statistics, or machine learning and with rudimentary knowledge of Python programming.

Genres: Programming, Technical

526 pages, Paperback

Published November 6, 2023

Reviews

[minhhai · Rating 3 out of 5]

The book covers basic probability, statistics and machine learning theories and generally how to use Python to implement them. It also gives a quick introduction to Python and its scientific tools such as numpy, scipy and scikit-learns.

The approach is quite pedagogical. Each topic is introduced first by fundamental, mathematical constructions, then important theorems or well-established results, and demonstrations with Python. It helps readers have a foundational knowledge of the topics.

However, it's not particularly useful for practical applications in real-world problems. The Python parts are mostly for illustrating the theories, rather than Python techniques to do useful tasks. The title of the book should have been "Fundamentals of Probability, Statistics, and Machine Learning - with Python examples".