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Название: Machine Learning in Action Автор: Peter Harrington Издательство: Manning Publications Год издания: 2012 Страниц: 384 ISBN: 1617290181, 978-1617290183 Язык: English Формат: PDF + EPUB Размер: 11.7 Мб
Machine Learning in Action is unique book that blends the foundational theories of machine learning with the practical realities of building tools for everyday data analysis. You’ll use the flexible Python programming language to build programs that implement algorithms for data classification, forecasting, recommendations, and higher-level features like summarization and simplification.
A machine is said to learn when its performance improves with experience. Learning requires algorithms and programs that capture data and ferret out the interesting or useful patterns. Once the specialized domain of analysts and mathematicians, machine learning is becoming a skill needed by many.
Machine Learning in Action is a clearly written tutorial for developers. It avoids academic language and takes you straight to the techniques you’ll use in your day-to-day work. Many (Python) examples present the core algorithms of statistical data processing, data analysis, and data visualization in code you can reuse. You’ll understand the concepts and how they fit in with tactical tasks like classification, forecasting, recommendations, and higher-level features like summarization and simplification.
Readers need no prior experience with machine learning or statistical processing. Familiarity with Python is helpful.
What’s inside A no-nonsense introduction Examples showing common ML tasks Everyday data analysis Implementing classic algorithms like Apriori and Adaboos
Table of Contents Part I: Classification Chapter 1. Machine learning basics Chapter 2. Classifying with k-Nearest Neighbors Chapter 3. Splitting datasets one feature at a time: decision trees Chapter 4. Classifying with probability theory: naive Bayes Chapter 5. Logistic regression Chapter 6. Support vector machines Chapter 7. Improving classification with the AdaBoost meta-algorithm
Part II: Forecasting numeric values with regression Chapter 8. Predicting numeric values: regression Chapter 9. Tree-based regression
Part III: Unsupervised learning Chapter 10. Grouping unlabeled items using k-means clustering Chapter 11. Association analysis with the Apriori algorithm Chapter 12. Efficiently finding frequent itemsets with FP-growth
Part IV: Additional tools Chapter 13. Using principal component analysis to simplify data Chapter 14. Simplifying data with the singular value decomposition Chapter 15. Big data and MapReduce
Appendix A. Getting started with Python Appendix B. Linear algebra Appendix C. Probability refresher Appendix D. Resources
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