{"product_id":"machine-learning-with-python-for-everyone-paperback","title":"Machine Learning with Python for Everyone (Paperback)","description":"\u003cp style=\"margin: 0px 0px 14px; padding: 0px; font-family: \" amazon ember arial sans-serif font-size:=\"\"\u003e\u003cspan style=\"font-size:14px;\"\u003e\u003cspan style=\"font-family:arial,helvetica,sans-serif;\"\u003eStudents are crushing to master powerful machine learning techniques for improving decision-making and scaling analysis to immense datasets. Machine learning with Python for everyone brings together all they'll need to succeed: a practical understanding of the machine learning process, accessible code, skills for implementing that process with Python and the scikit-learn library, and real expertise in using learning systems intelligently.Reflecting 20 years of experience teaching non-specialists, the author teaches through carefully-crafted datasets that are complex enough to be interesting, but simple enough for non-specialists. Building on this foundation, the book presents real-world case studies that apply his lessons in detailed, nuanced ways. Throughout, he offers clear narratives, practical “code-alongs,” and easy-to-understand images -- focusing on Mathematics only where it’s necessary to make connection and deepen insight.\u003c\/span\u003e\u003c\/span\u003e\u003c\/p\u003e\u003cp\u003e\u003cstrong\u003eTable Of Contents:\u003c\/strong\u003e\u003c\/p\u003e\u003cp\u003eChapter 1: Let’s Discuss Learning\u003c\/p\u003e\u003cp\u003eChapter 2: Predicting Categories: Getting Started With Classification\u003c\/p\u003e\u003cp\u003eChapter 3: Predicting Numerical Values: Getting Started With Regression\u003c\/p\u003e\u003cp\u003eChapter 4: Evaluating And Comparing Learners\u003c\/p\u003e\u003cp\u003eChapter 5: Evaluating Classifiers\u003c\/p\u003e\u003cp\u003eChapter 6: Evaluating Regressors\u003c\/p\u003e\u003cp\u003eChapter 7: More Classification Methods\u003c\/p\u003e\u003cp\u003eChapter 8: More Regression Methods\u003c\/p\u003e\u003cp\u003eChapter 9: Manual Feature Engineering: Manipulating Data For Fun And Profit\u003c\/p\u003e\u003cp\u003eChapter 10: Models That Engineer Features For Us\u003c\/p\u003e\u003cp\u003eChapter 11: Feature Engineering For Domains: Domain-Specific Learning Online Chapters\u003c\/p\u003e\u003cp\u003eChapter 12: Tuning Hyperparameters And Pipelines\u003c\/p\u003e\u003cp\u003eChapter 13: Combining Learners\u003c\/p\u003e\u003cp\u003eChapter 14: Connecting, Extensions, And Further Directions\u003cspan style=\"color: rgb(15, 17, 17); font-family: arial, helvetica, sans-serif; font-size: 14px; font-weight: 700;\"\u003e  \u003c\/span\u003e\u003c\/p\u003e\u003cp\u003e\u003cspan style=\"color: rgb(15, 17, 17); font-family: arial, helvetica, sans-serif; font-size: 14px; font-weight: 700;\"\u003e About the Author\u003c\/span\u003e\u003c\/p\u003e\u003cdiv class=\"a-section a-spacing-small a-padding-small\" style=\"box-sizing: border-box; margin-bottom: 0px; color: rgb(15, 17, 17); font-family: \" amazon ember arial sans-serif padding:=\"\"\u003e\u003cspan style=\"font-size:14px;\"\u003e\u003cspan style=\"font-family:arial,helvetica,sans-serif;\"\u003e\u003cstrong\u003eDr. Mark Fenner,\u003c\/strong\u003e owner of Fenner Training and Consulting, LLC, has taught computing and mathematics to diverse adult audiences since 1999, and holds a PhD in computer science. His research has included design, implementation, and performance of machine learning and numerical algorithms; developing learning systems to detect user anomalies; and probabilistic modeling of protein function\u003c\/span\u003e\u003c\/span\u003e\u003c\/div\u003e","brand":"Pearson Education","offers":[{"title":"Default Title","offer_id":50506222469190,"sku":"9789353944902","price":731.0,"currency_code":"INR","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0754\/2797\/1142\/files\/81gPCpfZpnL.jpg?v=1790323648","url":"https:\/\/cb-india.com\/products\/machine-learning-with-python-for-everyone-paperback","provider":"Computer Bookshop (India) Pvt Ltd","version":"1.0","type":"link"}