Practical Machine Learning With Python Access

(Edition 2)

Paul Ammann and Jeff Offutt

Notes & materials Last update
Table of Contents August 2016
Preface, with chapter mappings September 2016
Power Point SlidesSeptember 2022
Student Solution ManualDecember 2018

Contact authors for instructor solutions Send email to Jeff and Paul from your university email address, and include documentation that you are an instructor using the book (a class website, faculty list, etc.).

December 2018
In-Class ExercisesMarch 2017
Complete Programs From TextMarch 2019
Errata ListJune 2010
Support software 
Graph Coverage Web App (Ch 7)
Data Flow Coverage Web App (Ch 7)
Logic Coverage Web App (Ch 8)
DNF Logic Coverage Web App (Ch 8)
muJava Mutation Tool (Ch 9)
February 2017
Author’s course websitesLast taught
SWE 437 (Ammann)Fall 2018
SWE 637 (Ammann)Spring 2019
SWE 737 (Ammann)Spring 2018
SWE 437 (Offutt)Spring 2019
SWE 637 (Offutt)Fall 2018
SWE 737 (Offutt)Spring 2017
The authors donate all royalties from book sales to a scholarship fund for software engineering students at George Mason University.

Practical Machine Learning With Python Access

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: A broad overview of algorithms and a deep dive into the Python Machine Learning Ecosystem , covering essential libraries like Scikit-Learn. : A free, step-by-step roadmap for preparing data,

: A project-based video course that starts with environment setup (Anaconda/Jupyter) and moves into supervised and unsupervised learning. : A free
Practical Machine Learning with Python
Cover art by Peter Hoey
Practical Machine Learning with Python
Translation by Fatmah Assiri
Arabic page
 
Last modified: January 2022.