{"product_id":"ensemble-methods-for-machine-learning-9781617297137","title":"Ensemble Methods for Machine Learning","description":"\u003cb\u003eEnsemble machine learning combines the power of multiple machine learning approaches, working together to deliver models that are highly performant and highly accurate.\u003c\/b\u003e \u003cp\u003e\u003c\/p\u003eInside \u003ci\u003eEnsemble Methods for Machine Learning\u003c\/i\u003e you will find: \u003cp\u003e\u003c\/p\u003e \u003cul\u003e \u003cli\u003eMethods for classification, regression, and recommendations\u003c\/li\u003e \u003cli\u003eSophisticated off-the-shelf ensemble implementations\u003c\/li\u003e \u003cli\u003eRandom forests, boosting, and gradient boosting\u003c\/li\u003e \u003cli\u003eFeature engineering and ensemble diversity\u003c\/li\u003e \u003cli\u003eInterpretability and explainability for ensemble methods\u003c\/li\u003e \u003c\/ul\u003e \u003cbr\u003eEnsemble machine learning trains a diverse group of machine learning models to work together, aggregating their output to deliver richer results than a single model. Now in \u003ci\u003eEnsemble Methods for Machine Learning\u003c\/i\u003e you'll discover core ensemble methods that have proven records in both data science competitions and real-world applications. Hands-on case studies show you how each algorithm works in production. By the time you're done, you'll know the benefits, limitations, and practical methods of applying ensemble machine learning to real-world data, and be ready to build more explainable ML systems. \u003cp\u003e\u003c\/p\u003e Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications. \u003cp\u003e\u003c\/p\u003e \u003cb\u003eAbout the Technology \u003c\/b\u003e \u003cp\u003e\u003c\/p\u003e Automatically compare, contrast, and blend the output from multiple models to squeeze the best results from your data. Ensemble machine learning applies a \"wisdom of crowds\" method that dodges the inaccuracies and limitations of a single model. By basing responses on multiple perspectives, this innovative approach can deliver robust predictions even without massive datasets. \u003cp\u003e\u003c\/p\u003e \u003cb\u003eAbout the Book \u003c\/b\u003e \u003cp\u003e\u003c\/p\u003e \u003ci\u003eEnsemble Methods for Machine Learning\u003c\/i\u003e teaches you practical techniques for applying multiple ML approaches simultaneously. Each chapter contains a unique case study that demonstrates a fully functional ensemble method, with examples including medical diagnosis, sentiment analysis, handwriting classification, and more. There's no complex math or theory--you'll learn in a visuals-first manner, with ample code for easy experimentation! \u003cp\u003e\u003c\/p\u003e \u003cb\u003eWhat's Inside \u003c\/b\u003e \u003cp\u003e\u003c\/p\u003e \u003cul\u003e \u003cli\u003eBagging, boosting, and gradient boosting\u003c\/li\u003e \u003cli\u003eMethods for classification, regression, and retrieval\u003c\/li\u003e \u003cli\u003eInterpretability and explainability for ensemble methods\u003c\/li\u003e \u003cli\u003eFeature engineering and ensemble diversity\u003c\/li\u003e \u003c\/ul\u003e \u003cbr\u003e\u003cb\u003eAbout the Reader\u003c\/b\u003e \u003cp\u003e\u003c\/p\u003e For Python programmers with machine learning experience. \u003cp\u003e\u003c\/p\u003e \u003cb\u003eAbout the Author \u003c\/b\u003e \u003cp\u003e\u003c\/p\u003e Gautam Kunapuli has over 15 years of experience in academia and the machine learning industry. \u003cp\u003e\u003c\/p\u003e \u003cb\u003eTable of Contents\u003c\/b\u003e \u003cp\u003e\u003c\/p\u003e\u003cb\u003ePART 1 - THE BASICS OF ENSEMBLES\u003c\/b\u003e\u003cbr\u003e 1 Ensemble methods: Hype or hallelujah?\u003cbr\u003e \u003cb\u003ePART 2 - ESSENTIAL ENSEMBLE METHODS\u003c\/b\u003e\u003cbr\u003e 2 Homogeneous parallel ensembles: Bagging and random forests\u003cbr\u003e 3 Heterogeneous parallel ensembles: Combining strong learners\u003cbr\u003e 4 Sequential ensembles: Adaptive boosting\u003cbr\u003e 5 Sequential ensembles: Gradient boosting\u003cbr\u003e 6 Sequential ensembles: Newton boosting\u003cbr\u003e \u003cb\u003ePART 3 - ENSEMBLES IN THE WILD: ADAPTING ENSEMBLE METHODS TO YOUR DATA\u003c\/b\u003e\u003cbr\u003e 7 Learning with continuous and count labels\u003cbr\u003e 8 Learning with categorical features\u003cbr\u003e 9 Explaining your ensembles\u003cbr\u003e\u003cbr\u003e\u003cb\u003eAuthor:\u003c\/b\u003e \u003ca href=\"https:\/\/sureshotbooks-com.myshopify.com\/search?type=product%2Carticle%2Cpage\u0026amp;q=AUTH-6605367\"\u003eGautam Kunapuli\u003c\/a\u003e\u003cbr\u003e\u003cb\u003ePublisher:\u003c\/b\u003e Manning Publications\u003cbr\u003e\u003cb\u003ePublished:\u003c\/b\u003e 06\/09\/2023\u003cbr\u003e\u003cb\u003ePages:\u003c\/b\u003e 350\u003cbr\u003e\u003cb\u003eBinding Type:\u003c\/b\u003e Paperback\u003cbr\u003e\u003cb\u003eWeight:\u003c\/b\u003e 1.33lbs\u003cbr\u003e\u003cb\u003eSize:\u003c\/b\u003e 9.26h x 7.44w x 0.72d\u003cbr\u003e\u003cb\u003eISBN13:\u003c\/b\u003e 9781617297137\u003cbr\u003e\u003cb\u003eISBN10:\u003c\/b\u003e 1617297135\u003cbr\u003e\u003cb\u003eBISAC Categories:\u003c\/b\u003e\u003cbr\u003e- \u003ca href=\"https:\/\/sureshotbooks-com.myshopify.com\/search?type=product%2Carticle%2Cpage\u0026amp;q=CAT-COM\"\u003eComputers\u003c\/a\u003e | \u003ca href=\"https:\/\/sureshotbooks-com.myshopify.com\/search?type=product%2Carticle%2Cpage\u0026amp;q=BISAC-COM094000\"\u003eData Science | Machine Learning\u003c\/a\u003e\u003cbr\u003e- \u003ca href=\"https:\/\/sureshotbooks-com.myshopify.com\/search?type=product%2Carticle%2Cpage\u0026amp;q=CAT-COM\"\u003eComputers\u003c\/a\u003e | \u003ca href=\"https:\/\/sureshotbooks-com.myshopify.com\/search?type=product%2Carticle%2Cpage\u0026amp;q=BISAC-COM044000\"\u003eData Science | Neural Networks\u003c\/a\u003e\u003cbr\u003e- \u003ca href=\"https:\/\/sureshotbooks-com.myshopify.com\/search?type=product%2Carticle%2Cpage\u0026amp;q=CAT-COM\"\u003eComputers\u003c\/a\u003e | \u003ca href=\"https:\/\/sureshotbooks-com.myshopify.com\/search?type=product%2Carticle%2Cpage\u0026amp;q=BISAC-COM051360\"\u003eLanguages | Python\u003c\/a\u003e\u003cbr\u003e","brand":"Manning Publications","offers":[{"title":"Default Title","offer_id":44439176413421,"sku":"9781617297137","price":79.98,"currency_code":"USD","in_stock":false}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0550\/8097\/6621\/products\/img_7dd66771-085b-424e-bed7-32ac38a32ac2.jpg?v=1700156294","url":"https:\/\/sureshotbooks.com\/products\/ensemble-methods-for-machine-learning-9781617297137","provider":"SureShot Books Publishing LLC","version":"1.0","type":"link"}