Book description
Gain the confidence you need to apply machine learning in your daily work. With this practical guide, author Matthew Kirk shows you how to integrate and test machine learning algorithms in your code, without the academic subtext.
Featuring graphs and highlighted code examples throughout, the book features tests with Python’s Numpy, Pandas, ScikitLearn, and SciPy data science libraries. If you’re a software engineer or business analyst interested in data science, this book will help you:
 Reference realworld examples to test each algorithm through engaging, handson exercises
 Apply testdriven development (TDD) to write and run tests before you start coding
 Explore techniques for improving your machinelearning models with data extraction and feature development
 Watch out for the risks of machine learning, such as underfitting or overfitting data
 Work with KNearest Neighbors, neural networks, clustering, and other algorithms
Table of contents
 Preface
 1. Probably Approximately Correct Software
 2. A Quick Introduction to Machine Learning
 3. KNearest Neighbors
 4. Naive Bayesian Classification
 5. Decision Trees and Random Forests

6. Hidden Markov Models
 Tracking User Behavior Using State Machines
 Emissions/Observations of Underlying States
 Simplification Through the Markov Assumption
 Hidden Markov Model
 Evaluation: ForwardBackward Algorithm
 The Decoding Problem Through the Viterbi Algorithm
 The Learning Problem
 PartofSpeech Tagging with the Brown Corpus
 Conclusion
 7. Support Vector Machines
 8. Neural Networks
 9. Clustering
 10. Improving Models and Data Extraction
 11. Putting It Together: Conclusion
 Index
Product information
 Title: Thoughtful Machine Learning with Python
 Author(s):
 Release date: January 2017
 Publisher(s): O'Reilly Media, Inc.
 ISBN: 9781491924136
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