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, Scikit-Learn, and SciPy data science libraries. If you’re a software engineer or business analyst interested in data science, this book will help you:
- Reference real-world examples to test each algorithm through engaging, hands-on exercises
- Apply test-driven development (TDD) to write and run tests before you start coding
- Explore techniques for improving your machine-learning models with data extraction and feature development
- Watch out for the risks of machine learning, such as underfitting or overfitting data
- Work with K-Nearest Neighbors, neural networks, clustering, and other algorithms
Table of contents
- Preface
- 1. Probably Approximately Correct Software
- 2. A Quick Introduction to Machine Learning
- 3. K-Nearest 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: Forward-Backward Algorithm
- The Decoding Problem Through the Viterbi Algorithm
- The Learning Problem
- Part-of-Speech 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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