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Machine Learning in Action, Harrington, Peter

Author Name    Harrington, Peter

Title   Machine Learning in Action

Binding   PAPERBACK

Book Condition   Good

Publisher    Manning Publications

ISBN Number    1617290181 / 9781617290183

Seller ID   SKU9145442

1617290181 Good. Light to moderate wear to covers/corners. Trade paperback binding. Support Last Word Books & Press and independent booksellers.

Stock Description, May Not Reflect Item

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 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 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.

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 InsideA no-nonsense introductionExamples showing common ML tasksEveryday data analysisImplementing classic algorithms like Apriori and AdaboosTable of ContentsPART 1 CLASSIFICATIONMachine learning basicsClassifying with k-Nearest NeighborsSplitting datasets one feature at a time: decision treesClassifying with probability theory: naive BayesLogistic regressionSupport vector machinesImproving classification with the AdaBoost meta algorithmPART 2 FORECASTING NUMERIC VALUES WITH REGRESSIONPredicting numeric values: regressionTree-based regressionPART 3 UNSUPERVISED LEARNINGGrouping unlabeled items using k-means clusteringAssociation analysis with the Apriori algorithmEfficiently finding frequent itemsets with FP-growthPART 4 ADDITIONAL TOOLSUsing principal component analysis to simplify dataSimplifying data with the singular value decompositionBig data and MapReduce

Price = 35.00 USD


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