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Machine Learning in Action download pdf

Machine Learning in Action download pdf

Peter Harrington,: Machine Learning in Action

Machine Learning in Action


Description

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 introduction Examples showing common ML tasks Everyday data analysis Implementing classic algorithms like Apriori and AdaboosTable of ContentsPART 1 CLASSIFICATION Machine learning basics Classifying with k-Nearest Neighbors Splitting datasets one feature at a time: decision trees Classifying with probability theory: naive 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

"Ever seen a French child throw a tantrum in a restaurant or talk back to his parents? Neither has Catherine Crawford. In "French Twist" she uncovers the secrets of French child-rearing--and then tries them out on her own family, with remarkable results. Part memoir, part instruction manual, "French Twist" is hilarious, honest, and incredibly useful."--Lori Leibovich, executive lifestyle editor of The Huffington Post Machine Learning in Action free epub 'Hell is other people.' It is a chilling, page-turning Hammer novella by the Booker-Prize-winning author of Vernon God Little. The setting: a faded, lonely guesthouse on the Essex coast. Outside, it's dark, and very foggy. Inside there's no phone or internet reception, no connection with the outside world. Enter Ariel Panek, a promising young academic en route from the USA to an important convention in Amsterdam. With his plane grounded by fog at Stanstead, he has been booked in for the night at the guesthouse. Discombobulated and jetlagged, he falls in with a family who appear to be commemorating an event. But this is no ordinary celebration. And this is no ordinary family. As evening becomes night, Panek realises that he has become caught in an insidious web of other people's secrets and lies, a Sartrian hell from which for him there may be no escape.


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Author: Peter Harrington,
Number of Pages: 384 pages
Published Date: 19 Apr 2012
Publisher: Manning Publications
Publication Country: Shelter Island, N.Y., United Kingdom
Language: English
ISBN: 9781617290183
Download Link: Click Here
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