{"product_id":"9781638352457","title":"Machine Learning in Action","description":"\u003cb\u003eSummary\u003c\/b\u003e\u003cbr\u003e\u003cbr\u003e\u003ci\u003eMachine Learning in Action\u003c\/i\u003e 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.\u003cbr\u003e\u003cb\u003eAbout the Book\u003c\/b\u003e\u003cbr\u003eA 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.\u003cbr\u003e\u003cbr\u003e\u003ci\u003eMachine Learning in Action\u003c\/i\u003e 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.\u003cbr\u003e\u003cbr\u003eReaders need no prior experience with machine learning or statistical processing. Familiarity with Python is helpful.\u003cbr\u003e\u003cbr\u003e 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. \u003cbr\u003e\u003cb\u003eWhat's Inside\u003c\/b\u003e\u003cul\u003e\n\u003cli\u003eA no-nonsense introduction\u003c\/li\u003e\n\u003cli\u003eExamples showing common ML tasks\u003c\/li\u003e\n\u003cli\u003eEveryday data analysis\u003c\/li\u003e\n\u003cli\u003eImplementing classic algorithms like Apriori and Adaboos\u003c\/li\u003e\n\u003c\/ul\u003e\u003cb\u003eTable of Contents\u003c\/b\u003e\u003col\u003e\n\u003cstrong\u003ePART 1 CLASSIFICATION\u003c\/strong\u003e\u003cli\u003eMachine learning basics\u003c\/li\u003e\n\u003cli\u003eClassifying with k-Nearest Neighbors\u003c\/li\u003e\n\u003cli\u003eSplitting datasets one feature at a time: decision trees\u003c\/li\u003e\n\u003cli\u003eClassifying with probability theory: naïve Bayes\u003c\/li\u003e\n\u003cli\u003eLogistic regression\u003c\/li\u003e\n\u003cli\u003eSupport vector machines\u003c\/li\u003e\n\u003cli\u003eImproving classification with the AdaBoost meta algorithm\u003c\/li\u003e\n\u003cstrong\u003ePART 2 FORECASTING NUMERIC VALUES WITH REGRESSION\u003c\/strong\u003e\u003cli\u003ePredicting numeric values: regression\u003c\/li\u003e\n\u003cli\u003eTree-based regression\u003c\/li\u003e\n\u003cstrong\u003ePART 3 UNSUPERVISED LEARNING\u003c\/strong\u003e\u003cli\u003eGrouping unlabeled items using k-means clustering\u003c\/li\u003e\n\u003cli\u003eAssociation analysis with the Apriori algorithm\u003c\/li\u003e\n\u003cli\u003eEfficiently finding frequent itemsets with FP-growth\u003c\/li\u003e\n\u003cstrong\u003ePART 4 ADDITIONAL TOOLS\u003c\/strong\u003e\u003cli\u003eUsing principal component analysis to simplify data\u003c\/li\u003e\n\u003cli\u003eSimplifying data with the singular value decomposition\u003c\/li\u003e\n\u003cli\u003eBig data and MapReduce\u003c\/li\u003e\n\u003c\/ol\u003e","brand":"Manning","offers":[{"title":"Default Title","offer_id":46634647814385,"sku":"9781638352457","price":49.99,"currency_code":"USD","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0674\/5433\/7265\/files\/9781638352457_p0.jpg?v=1765422875","url":"https:\/\/shop.barnesandnoble.com\/products\/9781638352457","provider":"Barnes \u0026 Noble","version":"1.0","type":"link"}