Author: Matthew Kirk
Pub Date: 2017
Format: EPUB/MOBI/AZW3/PDF (conv)
Size: 16 Mb
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By teaching you how to code machine-learning algorithms using a test-driven approach, this practical book helps you gain the confidence you need to use machine learning effectively in a business environment. You’ll learn how to dissect algorithms at a granular level, using various tests, and discover a framework for testing machine learning code. The author provides real-world examples to demonstrate the results of using machine-learning code effectively.
Featuring graphs and highlighted code throughout, Thoughtful Machine Learning with Python guides you through the process of writing problem-solving code, and in the process teaches you how to approach problems through scientific deduction and clever algorithms.
Table of Contents
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
7. Support Vector Machines
8. Neural Networks
10. Improving Models and Data Extraction
11. Putting It Together: Conclusion