{"product_id":"models-and-algorithms-for-unlabelled-da","title":"Models And Algorithms For Unlabelled Da","description":"\u003cdiv class=\"product-page-section\" style='box-sizing: border-box; margin-bottom: 36px; color: rgb(51, 51, 51); font-family: Lato, -apple-system, BlinkMacSystemFont, \"avenir next\", avenir, \"helvetica neue\", helvetica, Ubuntu, roboto, noto, \"segoe ui\", arial, sans-serif; font-size: 17.25px; background-color: rgb(255, 255, 255);'\u003e  \u003cdiv class=\"product-page-section\" style=\"box-sizing: border-box; margin-bottom: 0px; padding-bottom: 0px;\"\u003e   \u003cb style=\"box-sizing: border-box;\"\u003eDiscover all-practical implementations of the key algorithms and models for handling unlabeled data. Full of case studies demonstrating how to apply each technique to real-world problems.\u003c\/b\u003e   \u003cbr style=\"box-sizing: border-box;\"\u003e   \u003cbr style=\"box-sizing: border-box;\"\u003eIn    \u003cb\u003e\u003ci style=\"box-sizing: border-box;\"\u003eModels and Algorithms for Unsupervised Learning\u003c\/i\u003e \u003c\/b\u003eyou''ll learn:   \u003cbr style=\"box-sizing: border-box;\"\u003e   \u003cbr style=\"box-sizing: border-box;\"\u003e   \u003cul style=\"box-sizing: border-box; margin-top: 0px; margin-bottom: 10.5px; padding-left: 17.5px;\"\u003e    \u003cli style=\"box-sizing: border-box;\"\u003eFundamental building blocks and concepts of machine learning and unsupervised learning\u003c\/li\u003e    \u003cli style=\"box-sizing: border-box;\"\u003eData cleaning for structured and unstructured data like text and images\u003c\/li\u003e    \u003cli style=\"box-sizing: border-box;\"\u003eUnsupervised time series clustering, Gaussian Mixture models, and statistical methods\u003c\/li\u003e    \u003cli style=\"box-sizing: border-box;\"\u003eBuilding neural networks such as GANs and autoencoders\u003c\/li\u003e    \u003cli style=\"box-sizing: border-box;\"\u003eHow to interpret the results of unsupervised learning\u003c\/li\u003e    \u003cli style=\"box-sizing: border-box;\"\u003eChoosing the right algorithm for your problem\u003c\/li\u003e    \u003cli style=\"box-sizing: border-box;\"\u003eDeploying unsupervised learning to production\u003c\/li\u003e    \u003cli style=\"box-sizing: border-box;\"\u003eBusiness use cases for machine learning and unsupervised learning\u003c\/li\u003e   \u003c\/ul\u003e   \u003cbr style=\"box-sizing: border-box;\"\u003e   \u003cbr style=\"box-sizing: border-box;\"\u003e   \u003ci style=\"box-sizing: border-box; margin-bottom: 0px; padding-bottom: 0px;\"\u003e\u003cb\u003eModels and Algorithms for Unsupervised Learning\u003c\/b\u003e\u003c\/i\u003e introduces mathematical techniques, key algorithms, and Python implementations that will help you build machine learning models for unannotated data. You''ll discover hands-off and unsupervised machine learning approaches that can still untangle raw, real-world datasets and support sound strategic decisions for your business. Don''t get bogged down in theory—the book bridges the gap between complex math and practical Python implementations, covering end-to-end model development all the way through to production deployment.  \u003c\/div\u003e \u003c\/div\u003e \u003cdiv class=\"product-page-section\" style='box-sizing: border-box; margin-bottom: 36px; color: rgb(51, 51, 51); font-family: Lato, -apple-system, BlinkMacSystemFont, \"avenir next\", avenir, \"helvetica neue\", helvetica, Ubuntu, roboto, noto, \"segoe ui\", arial, sans-serif; font-size: 17.25px; background-color: rgb(255, 255, 255);'\u003e  \u003ch2 style=\"box-sizing: border-box; line-height: 1.1; color: inherit; margin: 0px 0px 10px; font-size: 27px; text-transform: lowercase;\"\u003eabout the technology\u003c\/h2\u003e  \u003ca name=\"about-the-technology\" class=\"anchor\" style=\"box-sizing: border-box; background-color: transparent; color: rgb(64, 127, 191); visibility: hidden; display: block; position: relative; margin-bottom: 0px; padding-bottom: 0px;\"\u003e\u003c\/a\u003eUnsupervised learning and machine learning algorithms draw inferences from unannotated data sets. The self-organizing approach to machine learning is great for spotting patterns a human might miss. \u003c\/div\u003e \u003cdiv class=\"product-page-section\" style='box-sizing: border-box; margin-bottom: 36px; color: rgb(51, 51, 51); font-family: Lato, -apple-system, BlinkMacSystemFont, \"avenir next\", avenir, \"helvetica neue\", helvetica, Ubuntu, roboto, noto, \"segoe ui\", arial, sans-serif; font-size: 17.25px; background-color: rgb(255, 255, 255);'\u003e  \u003ch2 style=\"box-sizing: border-box; line-height: 1.1; color: inherit; margin: 0px 0px 10px; font-size: 27px; text-transform: lowercase;\"\u003eabout the book\u003c\/h2\u003e  \u003ca name=\"about-the-book\" class=\"anchor\" style=\"box-sizing: border-box; background-color: transparent; color: rgb(64, 127, 191); visibility: hidden; display: block; position: relative;\"\u003e\u003c\/a\u003e  \u003ci style=\"box-sizing: border-box; margin-bottom: 0px; padding-bottom: 0px;\"\u003e\u003cb\u003eModels and Algorithms for Unsupervised Learning\u003c\/b\u003e\u003c\/i\u003e teaches you to apply a full spectrum of machine learning algorithms to raw data. You''ll master everything from kmeans and hierarchical clustering, to advanced neural networks like GANs and Restricted Boltzmann Machines. You''ll learn the business use case for different models, and master best practices for structured, text, and image data. Each new algorithm is introduced with a case study for retail, aviation, banking, and more—and you''ll develop a Python solution to fix each of these real-world problems. At the end of each chapter, you''ll find quizzes, practice datasets, and links to research papers to help you lock in what you''ve learned and expand your knowledge. \u003c\/div\u003e \u003cdiv class=\"product-page-section\" style='box-sizing: border-box; margin-bottom: 36px; color: rgb(51, 51, 51); font-family: Lato, -apple-system, BlinkMacSystemFont, \"avenir next\", avenir, \"helvetica neue\", helvetica, Ubuntu, roboto, noto, \"segoe ui\", arial, sans-serif; font-size: 17.25px; background-color: rgb(255, 255, 255);'\u003e  \u003cdiv class=\"row\" style=\"box-sizing: border-box; margin-left: -18px; margin-right: -18px; margin-bottom: 0px; padding-bottom: 0px;\"\u003e   \u003cdiv class=\"col-sm-12\" style=\"box-sizing: border-box; position: relative; min-height: 1px; padding-left: 18px; padding-right: 18px; float: left; width: 570px;\"\u003e    \u003cdiv class=\"form-group\" style=\"box-sizing: border-box; margin-bottom: 15px;\"\u003e     \u003cdiv class=\"input-group\" style=\"box-sizing: border-box; position: relative; display: table; border-collapse: separate;\"\u003e\u003c\/div\u003e    \u003c\/div\u003e   \u003c\/div\u003e  \u003c\/div\u003e \u003c\/div\u003e","brand":"MediaPlace","offers":[{"title":"Default Title","offer_id":57312790184318,"sku":"NW9781617298721","price":53.95,"currency_code":"EUR","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0817\/1379\/1261\/files\/9781617298721.jpg?v=1778589665","url":"https:\/\/mediaplace.com\/en-eu\/products\/models-and-algorithms-for-unlabelled-da","provider":"MediaPlace","version":"1.0","type":"link"}