(Ebook) Machine Learning in Action by Peter Harrington ISBN 9781617290183, 1617290181

(Ebook) Machine Learning in Action by Peter Harrington ISBN 9781617290183, 1617290181

SummaryMachine 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 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 CLASSIFICATION Machine learning basicsClassifying with k-Nearest NeighborsSplitting datasets one feature at a time: decision treesClassifying with probability theory: naГЇve BayesLogistic regressionSupport vector machinesImproving classification with the AdaBoost meta algorithm PART 2 FORECASTING NUMERIC VALUES WITH REGRESSION Predicting numeric values: regressionTree-based regression PART 3 UNSUPERVISED LEARNING Grouping unlabeled items using k-means clusteringAssociation analysis with the Apriori algorithmEfficiently finding frequent itemsets with FP-growth PART 4 ADDITIONAL TOOLS Using principal component analysis to simplify dataSimplifying data with the singular value decompositionBig data and MapReduce