{"product_id":"grokking-machine-learning","title":"Grokking Machine Learning","description":"\u003cp\u003e\u003cspan style=\"box-sizing: border-box; font-weight: 700; color: rgb(15, 17, 17); font-family: \" amazon ember arial sans-serif font-size:\u003eDiscover valuable machine learning techniques you can understand and apply using just high-school math.\u003c\/span\u003e\u003cbr style=\"box-sizing: border-box; color: rgb(15, 17, 17); font-family: \" amazon ember arial sans-serif font-size:\u003e\u003cbr style=\"box-sizing: border-box; color: rgb(15, 17, 17); font-family: \" amazon ember arial sans-serif font-size:\u003e\u003cspan style=\"color: rgb(15, 17, 17); font-family: \" amazon ember arial sans-serif font-size:\u003eIn \u003c\/span\u003e\u003ci style=\"box-sizing: border-box; color: rgb(15, 17, 17); font-family: \" amazon ember arial sans-serif font-size:\u003eGrokking Machine Learning\u003c\/i\u003e\u003cspan style=\"color: rgb(15, 17, 17); font-family: \" amazon ember arial sans-serif font-size:\u003e you will learn:\u003c\/span\u003e\u003cbr style=\"box-sizing: border-box; color: rgb(15, 17, 17); font-family: \" amazon ember arial sans-serif font-size:\u003e\u003cbr style=\"box-sizing: border-box; color: rgb(15, 17, 17); font-family: \" amazon ember arial sans-serif font-size:\u003e\u003cspan style=\"color: rgb(15, 17, 17); font-family: \" amazon ember arial sans-serif font-size:\u003e    Supervised algorithms for classifying and splitting data\u003c\/span\u003e\u003cbr style=\"box-sizing: border-box; color: rgb(15, 17, 17); font-family: \" amazon ember arial sans-serif font-size:\u003e\u003cspan style=\"color: rgb(15, 17, 17); font-family: \" amazon ember arial sans-serif font-size:\u003e    Methods for cleaning and simplifying data\u003c\/span\u003e\u003cbr style=\"box-sizing: border-box; color: rgb(15, 17, 17); font-family: \" amazon ember arial sans-serif font-size:\u003e\u003cspan style=\"color: rgb(15, 17, 17); font-family: \" amazon ember arial sans-serif font-size:\u003e    Machine learning packages and tools\u003c\/span\u003e\u003cbr style=\"box-sizing: border-box; color: rgb(15, 17, 17); font-family: \" amazon ember arial sans-serif font-size:\u003e\u003cspan style=\"color: rgb(15, 17, 17); font-family: \" amazon ember arial sans-serif font-size:\u003e    Neural networks and ensemble methods for complex datasets\u003c\/span\u003e\u003cbr style=\"box-sizing: border-box; color: rgb(15, 17, 17); font-family: \" amazon ember arial sans-serif font-size:\u003e\u003cbr style=\"box-sizing: border-box; color: rgb(15, 17, 17); font-family: \" amazon ember arial sans-serif font-size:\u003e\u003ci style=\"box-sizing: border-box; color: rgb(15, 17, 17); font-family: \" amazon ember arial sans-serif font-size:\u003eGrokking Machine Learning\u003c\/i\u003e\u003cspan style=\"color: rgb(15, 17, 17); font-family: \" amazon ember arial sans-serif font-size:\u003e teaches you how to apply ML to your projects using only standard Python code and high school-level math. No specialist knowledge is required to tackle the hands-on exercises using Python and readily available machine learning tools. Packed with easy-to-follow Python-based exercises and mini-projects, this book sets you on the path to becoming a machine learning expert.\u003c\/span\u003e\u003cbr style=\"box-sizing: border-box; color: rgb(15, 17, 17); font-family: \" amazon ember arial sans-serif font-size:\u003e\u003cbr style=\"box-sizing: border-box; color: rgb(15, 17, 17); font-family: \" amazon ember arial sans-serif font-size:\u003e\u003cspan style=\"color: rgb(15, 17, 17); font-family: \" amazon ember arial sans-serif font-size:\u003ePurchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications.\u003c\/span\u003e\u003cbr style=\"box-sizing: border-box; color: rgb(15, 17, 17); font-family: \" amazon ember arial sans-serif font-size:\u003e\u003cbr style=\"box-sizing: border-box; color: rgb(15, 17, 17); font-family: \" amazon ember arial sans-serif font-size:\u003e\u003cstrong\u003e\u003cspan style=\"color: rgb(15, 17, 17); font-family: \" amazon ember arial sans-serif font-size:\u003eAbout the technology\u003c\/span\u003e\u003c\/strong\u003e\u003cbr style=\"box-sizing: border-box; color: rgb(15, 17, 17); font-family: \" amazon ember arial sans-serif font-size:\u003e\u003cspan style=\"color: rgb(15, 17, 17); font-family: \" amazon ember arial sans-serif font-size:\u003eDiscover powerful machine learning techniques you can understand and apply using only high school math! Put simply, machine learning is a set of techniques for data analysis based on algorithms that deliver better results as you give them more data. ML powers many cutting-edge technologies, such as recommendation systems, facial recognition software, smart speakers, and even self-driving cars. This unique book introduces the core concepts of machine learning, using relatable examples, engaging exercises, and crisp illustrations.\u003c\/span\u003e\u003cbr style=\"box-sizing: border-box; color: rgb(15, 17, 17); font-family: \" amazon ember arial sans-serif font-size:\u003e\u003cbr style=\"box-sizing: border-box; color: rgb(15, 17, 17); font-family: \" amazon ember arial sans-serif font-size:\u003e\u003cstrong\u003e\u003cspan style=\"color: rgb(15, 17, 17); font-family: \" amazon ember arial sans-serif font-size:\u003eAbout the book\u003c\/span\u003e\u003c\/strong\u003e\u003cbr style=\"box-sizing: border-box; color: rgb(15, 17, 17); font-family: \" amazon ember arial sans-serif font-size:\u003e\u003ci style=\"box-sizing: border-box; color: rgb(15, 17, 17); font-family: \" amazon ember arial sans-serif font-size:\u003eGrokking Machine Learning\u003c\/i\u003e\u003cspan style=\"color: rgb(15, 17, 17); font-family: \" amazon ember arial sans-serif font-size:\u003e presents machine learning algorithms and techniques in a way that anyone can understand. This book skips the confused academic jargon and offers clear explanations that require only basic algebra. As you go, you’ll build interesting projects with Python, including models for spam detection and image recognition. You’ll also pick up practical skills for cleaning and preparing data.\u003c\/span\u003e\u003cbr style=\"box-sizing: border-box; color: rgb(15, 17, 17); font-family: \" amazon ember arial sans-serif font-size:\u003e\u003cbr style=\"box-sizing: border-box; color: rgb(15, 17, 17); font-family: \" amazon ember arial sans-serif font-size:\u003e\u003cstrong\u003e\u003cspan style=\"color: rgb(15, 17, 17); font-family: \" amazon ember arial sans-serif font-size:\u003eWhat's inside\u003c\/span\u003e\u003c\/strong\u003e\u003cbr style=\"box-sizing: border-box; color: rgb(15, 17, 17); font-family: \" amazon ember arial sans-serif font-size:\u003e\u003cbr style=\"box-sizing: border-box; color: rgb(15, 17, 17); font-family: \" amazon ember arial sans-serif font-size:\u003e\u003cspan style=\"color: rgb(15, 17, 17); font-family: \" amazon ember arial sans-serif font-size:\u003e    Supervised algorithms for classifying and splitting data\u003c\/span\u003e\u003cbr style=\"box-sizing: border-box; color: rgb(15, 17, 17); font-family: \" amazon ember arial sans-serif font-size:\u003e\u003cspan style=\"color: rgb(15, 17, 17); font-family: \" amazon ember arial sans-serif font-size:\u003e    Methods for cleaning and simplifying data\u003c\/span\u003e\u003cbr style=\"box-sizing: border-box; color: rgb(15, 17, 17); font-family: \" amazon ember arial sans-serif font-size:\u003e\u003cspan style=\"color: rgb(15, 17, 17); font-family: \" amazon ember arial sans-serif font-size:\u003e    Machine learning packages and tools\u003c\/span\u003e\u003cbr style=\"box-sizing: border-box; color: rgb(15, 17, 17); font-family: \" amazon ember arial sans-serif font-size:\u003e\u003cspan style=\"color: rgb(15, 17, 17); font-family: \" amazon ember arial sans-serif font-size:\u003e    Neural networks and ensemble methods for complex datasets\u003c\/span\u003e\u003cbr style=\"box-sizing: border-box; color: rgb(15, 17, 17); font-family: \" amazon ember arial sans-serif font-size:\u003e\u003cbr style=\"box-sizing: border-box; color: rgb(15, 17, 17); font-family: \" amazon ember arial sans-serif font-size:\u003e\u003cstrong\u003e\u003cspan style=\"color: rgb(15, 17, 17); font-family: \" amazon ember arial sans-serif font-size:\u003eAbout the reader\u003c\/span\u003e\u003c\/strong\u003e\u003cbr style=\"box-sizing: border-box; color: rgb(15, 17, 17); font-family: \" amazon ember arial sans-serif font-size:\u003e\u003cspan style=\"color: rgb(15, 17, 17); font-family: \" amazon ember arial sans-serif font-size:\u003eFor readers who know basic Python. No machine learning knowledge necessary.\u003c\/span\u003e\u003cbr style=\"box-sizing: border-box; color: rgb(15, 17, 17); font-family: \" amazon ember arial sans-serif font-size:\u003e\u003cbr style=\"box-sizing: border-box; color: rgb(15, 17, 17); font-family: \" amazon ember arial sans-serif font-size:\u003e\u003cstrong\u003e\u003cspan style=\"color: rgb(15, 17, 17); font-family: \" amazon ember arial sans-serif font-size:\u003eAbout the author\u003c\/span\u003e\u003c\/strong\u003e\u003cbr style=\"box-sizing: border-box; color: rgb(15, 17, 17); font-family: \" amazon ember arial sans-serif font-size:\u003e\u003cspan style=\"box-sizing: border-box; font-weight: 700; color: rgb(15, 17, 17); font-family: \" amazon ember arial sans-serif font-size:\u003eLuis G. Serrano\u003c\/span\u003e\u003cspan style=\"color: rgb(15, 17, 17); font-family: \" amazon ember arial sans-serif font-size:\u003e is a research scientist in quantum artificial intelligence. Previously, he was a Machine Learning Engineer at Google and Lead Artificial Intelligence Educator at Apple.\u003c\/span\u003e\u003cbr style=\"box-sizing: border-box; color: rgb(15, 17, 17); font-family: \" amazon ember arial sans-serif font-size:\u003e\u003cbr style=\"box-sizing: border-box; color: rgb(15, 17, 17); font-family: \" amazon ember arial sans-serif font-size:\u003e\u003cstrong\u003e\u003cspan style=\"color: rgb(15, 17, 17); font-family: \" amazon ember arial sans-serif font-size:\u003eTable of Contents:\u003c\/span\u003e\u003c\/strong\u003e\u003cbr style=\"box-sizing: border-box; color: rgb(15, 17, 17); font-family: \" amazon ember arial sans-serif font-size:\u003e\u003cspan style=\"color: rgb(15, 17, 17); font-family: \" amazon ember arial sans-serif font-size:\u003e1 What is machine learning? It is common sense, except done by a computer\u003c\/span\u003e\u003cbr style=\"box-sizing: border-box; color: rgb(15, 17, 17); font-family: \" amazon ember arial sans-serif font-size:\u003e\u003cspan style=\"color: rgb(15, 17, 17); font-family: \" amazon ember arial sans-serif font-size:\u003e2 Types of machine learning\u003c\/span\u003e\u003cbr style=\"box-sizing: border-box; color: rgb(15, 17, 17); font-family: \" amazon ember arial sans-serif font-size:\u003e\u003cspan style=\"color: rgb(15, 17, 17); font-family: \" amazon ember arial sans-serif font-size:\u003e3 Drawing a line close to our points: Linear regression\u003c\/span\u003e\u003cbr style=\"box-sizing: border-box; color: rgb(15, 17, 17); font-family: \" amazon ember arial sans-serif font-size:\u003e\u003cspan style=\"color: rgb(15, 17, 17); font-family: \" amazon ember arial sans-serif font-size:\u003e4 Optimizing the training process: Underfitting, overfitting, testing, and regularization\u003c\/span\u003e\u003cbr style=\"box-sizing: border-box; color: rgb(15, 17, 17); font-family: \" amazon ember arial sans-serif font-size:\u003e\u003cspan style=\"color: rgb(15, 17, 17); font-family: \" amazon ember arial sans-serif font-size:\u003e5 Using lines to split our points: The perceptron algorithm\u003c\/span\u003e\u003cbr style=\"box-sizing: border-box; color: rgb(15, 17, 17); font-family: \" amazon ember arial sans-serif font-size:\u003e\u003cspan style=\"color: rgb(15, 17, 17); font-family: \" amazon ember arial sans-serif font-size:\u003e6 A continuous approach to splitting points: Logistic classifiers\u003c\/span\u003e\u003cbr style=\"box-sizing: border-box; color: rgb(15, 17, 17); font-family: \" amazon ember arial sans-serif font-size:\u003e\u003cspan style=\"color: rgb(15, 17, 17); font-family: \" amazon ember arial sans-serif font-size:\u003e7 How do you measure classification models? Accuracy and its friends\u003c\/span\u003e\u003cbr style=\"box-sizing: border-box; color: rgb(15, 17, 17); font-family: \" amazon ember arial sans-serif font-size:\u003e\u003cspan style=\"color: rgb(15, 17, 17); font-family: \" amazon ember arial sans-serif font-size:\u003e8 Using probability to its maximum: The naive Bayes model\u003c\/span\u003e\u003cbr style=\"box-sizing: border-box; color: rgb(15, 17, 17); font-family: \" amazon ember arial sans-serif font-size:\u003e\u003cspan style=\"color: rgb(15, 17, 17); font-family: \" amazon ember arial sans-serif font-size:\u003e9 Splitting data by asking questions: Decision trees\u003c\/span\u003e\u003cbr style=\"box-sizing: border-box; color: rgb(15, 17, 17); font-family: \" amazon ember arial sans-serif font-size:\u003e\u003cspan style=\"color: rgb(15, 17, 17); font-family: \" amazon ember arial sans-serif font-size:\u003e10 Combining building blocks to gain more power: Neural networks\u003c\/span\u003e\u003cbr style=\"box-sizing: border-box; color: rgb(15, 17, 17); font-family: \" amazon ember arial sans-serif font-size:\u003e\u003cspan style=\"color: rgb(15, 17, 17); font-family: \" amazon ember arial sans-serif font-size:\u003e11 Finding boundaries with style: Support vector machines and the kernel method\u003c\/span\u003e\u003cbr style=\"box-sizing: border-box; color: rgb(15, 17, 17); font-family: \" amazon ember arial sans-serif font-size:\u003e\u003cspan style=\"color: rgb(15, 17, 17); font-family: \" amazon ember arial sans-serif font-size:\u003e12 Combining models to maximize results: Ensemble learning\u003c\/span\u003e\u003cbr style=\"box-sizing: border-box; color: rgb(15, 17, 17); font-family: \" amazon ember arial sans-serif font-size:\u003e\u003cspan style=\"color: rgb(15, 17, 17); font-family: \" amazon ember arial sans-serif font-size:\u003e13 Putting it all in practice: A real-life example of data engineering and machine learning\u003c\/span\u003e\u003c\/p\u003e\u003cp\u003e\u003cbr\u003e\u003c\/p\u003e\u003ch3\u003eAbout the Author\u003c\/h3\u003e\u003cp\u003e\u003cb\u003eChrissy LeMaire\u003c\/b\u003e is a dual Microsoft MVP, awarded for her work with both SQL Server and PowerShell. She is an international speaker and the creator of the popular PowerShell module, dbatools. She has over 20 years of IT experience and currently works for General Dynamics Information Technology at NATO Special Ops HQ in Belgium.\u003c\/p\u003e\u003cp\u003e\u003cb\u003eBrandon Abshire\u003c\/b\u003e is the technical manager of the Epic Systems and Database Administration teams at Cedars-Sinai in Los Angeles, California\u003c\/p\u003e\u003cp\u003e\u003cb\u003eEbook License\u003c\/b\u003e\u003c\/p\u003e\u003cp\u003eEnd-User Warranty And License Agreement\u003c\/p\u003e\u003cp\u003e1. Grant Of License\u003c\/p\u003e\u003cp\u003eManning Has Authorized The Download By You Of An Unrestricted Number Of Copies Of The Electronic Book (Ebook) In Any Of The Available Formats. Manning Grants You A Nonexclusive, Nontransferable License To Use The Ebook According To The Terms And Conditions Herein. This License Agreement Permits You To Install The Ebook On Any And All Your Devices For Your Personal Use Only.\u003c\/p\u003e\u003cp\u003e2. Restrictions\u003c\/p\u003e\u003cp\u003eYou Shall Not: (1) Share, Resell, Rent, Assign, Timeshare, Distribute, Or Transfer All Or Part Of The Ebook Or Any Rights Granted Hereunder To Any Other Person; (2) Duplicate The Ebook, Except For A Single Backup Or Archival Copy; (3) Remove Any Proprietary Notices, Labels, Or Marks From The Ebook; (4) Transfer Or Sublicense Title To The Ebook To Any Other Party.\u003c\/p\u003e\u003cp\u003e3. Intellectual Property Protection\u003c\/p\u003e\u003cp\u003eThe Ebook Is Owned By Manning And Is Protected By United States And International Copyright And Other Intellectual Property Laws. Manning Reserves All Rights In The Ebook Not Expressly Granted Herein. This License And Your Right To Use The Ebook Terminate Automatically If You Violate Any Part Of This Agreement. In The Event Of Termination, You Must Remove The Original And Any Copies Of The Ebook From All Your Devices.\u003c\/p\u003e\u003cp\u003e4. Source Code Supplementary Material\u003c\/p\u003e\u003cp\u003eAny Source Code Files Provided As A Supplement To The Book Are Freely Available To The Public For Download. Reuse Of The Code Is Permitted, In Whole Or In Part, Including The Creation Of Derivative Works, Provided That You Acknowledge That You Are Using It And Identify The Source: Title, Publisher And Year.\u003c\/p\u003e\u003cp\u003e\u003cbr\u003e\u003c\/p\u003e","brand":"Manning Publications","offers":[{"title":"Paperback","offer_id":50506232627270,"sku":"9781617295911","price":5318.0,"currency_code":"INR","in_stock":true},{"title":"E-Book","offer_id":50506232660038,"sku":"9781617295911-2","price":4333.0,"currency_code":"INR","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0754\/2797\/1142\/files\/9781617295911.jpg?v=1790323853","url":"https:\/\/cb-india.com\/products\/grokking-machine-learning","provider":"Computer Bookshop (India) Pvt Ltd","version":"1.0","type":"link"}