Machine Learning in Action
Language: English
Published by Manning Publications, 2012
Series: Book 64 of 182 - In Action
- Softcover
- Used
Seller: Wonder Book, Frederick, MD, U.S.A.Wonder Book
AbeBooks seller since November 1, 1997
Condition: Used - Good
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Good condition. A copy that has been read but remains intact. May contain markings such as bookplates, stamps, limited notes and highlighting, or a few light stains.
Seller Inventory # L07C-01074
- Title
- Machine Learning in Action
- Author
- Harrington, Peter
- Publisher
- Manning Publications
- Publication year
- 2012
- Condition
- Good
- Binding
- Soft cover
- Language
- English
- ISBN 10
- 1617290181
- ISBN 13
- 9781617290183
- Series
- Book 64 of 182: In Action
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 Book
A 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.
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 1 CLASSIFICATION
- 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 PART 2 FORECASTING NUMERIC VALUES WITH REGRESSION
- Predicting numeric values: regression
- Tree-based regression PART 3 UNSUPERVISED LEARNING
- Grouping unlabeled items using k-means clustering
- Association analysis with the Apriori algorithm
- Efficiently finding frequent itemsets with FP-growth PART 4 ADDITIONAL TOOLS
- Using principal component analysis to simplify data
- Simplifying data with the singular value decomposition
- Big data and MapReduce
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About the Author
"About the title" may belong to another edition of this title.
Wonder Book
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