Machine learning in action
Language: English
Published by Manning Publications Co., 2012
Series: Book 64 of 182 - In Action
- Softcover
- Used
Condition: Used - Very good
£ 14.93
Quantity: 1 available
Add to basketItem description from seller
: Aprende a implementar algoritmos de machine learning con este manual práctico. Peter Harrington te guía a través de los conceptos clave y las técnicas esenciales, con ejemplos claros y aplicaciones reales. Descubre cómo construir modelos predictivos y resolver problemas complejos utilizando el poder del machine learning. EAN: 9781617290183 Tipo: Libros Categoría: Tecnología Título: Machine learning in action Autor: Peter Harrington Editorial: Manning Publications Co. Idioma: en Páginas: 354.
Seller Inventory # Happ-2026-10-15-aceb30d7
- Title
- Machine learning in action
- Author
- Peter Harrington
- Publisher
- Manning Publications Co.
- Publication year
- 2012
- Condition
- Very Good
- Binding
- Soft cover
- Language
- English
- ISBN 10
- 1617290181
- ISBN 13
- 9781617290183
- Series
- Book 64 of 182: In Action
Summary
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.
About the BookA machine is said to learn when its performance improves with experience. Learning requires algorithms and programs that capture data and ferret out the interestingor 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.
Purchase of the print book comes with an offer of a free PDF, ePub, and Kindle eBook from Manning. Also available is all code from the book.
What's Inside- A no-nonsense introduction
- Examples showing common ML tasks
- Everyday data analysis
- Implementing classic algorithms like Apriori and Adaboos
- Machine learning basics
- Classifying with k-Nearest Neighbors
- Splitting datasets one feature at a time: decision trees
- Classifying with probability theory: naïve Bayes
- Logistic regression
- Support vector machines
- Improving classification with the AdaBoost meta algorithm
- Predicting numeric values: regression
- Tree-based regression
- Grouping unlabeled items using k-means clustering
- Association analysis with the Apriori algorithm
- Efficiently finding frequent itemsets with FP-growth
- Using principal component analysis to simplify data
- Simplifying data with the singular value decomposition
- Big data and MapReduce
PART 1 CLASSIFICATION
PART 2 FORECASTING NUMERIC VALUES WITH REGRESSION
PART 3 UNSUPERVISED LEARNING
PART 4 ADDITIONAL TOOLS
"Synopsis" may belong to another edition of this title.
About the Author
Peter Harrington holds a Bachelors and a Masters Degrees in Electrical Engineering. He is a professional developer and data scientist. Peter holds five US patents and his work has been published in numerous academic journals.
Peter Harrington holds a Bachelors and a Masters Degrees in Electrical Engineering. He is a professional developer and data scientist. Peter holds five US patents and his work has been published in numerous academic journals.
"About the title" may belong to another edition of this title.
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