Advertisement

Python for Probability, Statistics, and Machine Learning

  • José Unpingco

Table of contents

  1. Front Matter
    Pages i-xv
  2. José Unpingco
    Pages 1-33
  3. José Unpingco
    Pages 35-100
  4. José Unpingco
    Pages 101-196
  5. José Unpingco
    Pages 197-273
  6. Back Matter
    Pages 275-276

About this book

Introduction

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.
  • 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.

Keywords

IPython Notebooks Machine Learning Probability and Statistics Python Toolchain Scientific Python

Authors and affiliations

  • José Unpingco
    • 1
  1. 1.San DiegoUSA

Bibliographic information

  • DOI https://doi.org/10.1007/978-3-319-30717-6
  • Copyright Information Springer International Publishing Switzerland 2016
  • Publisher Name Springer, Cham
  • eBook Packages Engineering
  • Print ISBN 978-3-319-30715-2
  • Online ISBN 978-3-319-30717-6
  • Buy this book on publisher's site
Industry Sectors
Automotive
Chemical Manufacturing
Electronics
IT & Software
Telecommunications
Aerospace
Oil, Gas & Geosciences
Engineering