Kindle Price: £69.34

Save £20.65 (23%)

includes VAT*
* Unlike print books, digital books are subject to VAT.

These promotions will be applied to this item:

Some promotions may be combined; others are not eligible to be combined with other offers. For details, please see the Terms & Conditions associated with these promotions.

Deliver to your Kindle or other device

Deliver to your Kindle or other device

Python for Probability, Statistics, and Machine Learning by [Unpingco, José]
Kindle App Ad

Python for Probability, Statistics, and Machine Learning 1st ed. 2016 Edition, Kindle Edition

See all 3 formats and editions Hide other formats and editions
Amazon Price
New from Used from
Kindle Edition

Kindle Monthly Deal
Browse a new selection of discounted Kindle Books each month. Shop now

Product description


"The purpose of this book is to introduce scientific Python to those who have a prior knowledge of probability and statistics as well as basic Python. ... this is a very valuable reference for those wishing to use these methods in a Python environment. ... I would strongly recommend this book for the intended audience or as a reference work. ... All in all, I strongly recommend this book for those who want to use Python in this area." (David E. Booth, Technometrics, Vol. 59 (2), April, 2017)

From the Back Cover

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.

Product details

  • Format: Kindle Edition
  • File Size: 22044 KB
  • Print Length: 276 pages
  • Publisher: Springer; 1st ed. 2016 edition (16 Mar. 2016)
  • Sold by: Amazon Media EU S.à r.l.
  • Language: English
  • ASIN: B01D2O0A8M
  • Text-to-Speech: Enabled
  • X-Ray:
  • Word Wise: Not Enabled
  • Enhanced Typesetting: Enabled
  • Average Customer Review: Be the first to review this item
  • Amazon Bestsellers Rank: #1,742,480 Paid in Kindle Store (See Top 100 Paid in Kindle Store)
  • Would you like to tell us about a lower price?

Customer reviews

There are no customer reviews yet.
Share your thoughts with other customers

Most helpful customer reviews on 4.5 out of 5 stars 2 reviews
Dimitri Shvorob
5.0 out of 5 starsWhat's with the price?
31 October 2016 - Published on
24 people found this helpful.
anonymous object
4.0 out of 5 starsUses python 2
26 May 2018 - Published on
click to open popover

Where's My Stuff?

Delivery and Returns

Need Help?