{"product_id":"hands-on-machine-learning-with-scikit-learn-and-scientific-python-toolkits-a-practical-guide-to-implementing-supervised-and-unsupervised-machine-lear-paperback","title":"Hands-On Machine Learning with scikit-learn and Scientific Python Toolkits: A practical guide to implementing supervised and unsupervised machine lear - Paperback","description":"\u003cp\u003eby \u003cb\u003eTarek Amr\u003c\/b\u003e (Author)\u003c\/p\u003e\u003cp\u003e\u003c\/p\u003e\u003cp\u003e\u003cstrong\u003eIntegrate scikit-learn with various tools such as NumPy, pandas, imbalanced-learn, and scikit-surprise and use it to solve real-world machine learning problems\u003c\/strong\u003e\u003c\/p\u003e\u003cp\u003e\u003cstrong\u003eKey Features\u003c\/strong\u003e\u003c\/p\u003e \u003cul\u003e \u003cli\u003eDelve into machine learning with this comprehensive guide to scikit-learn and scientific Python\u003c\/li\u003e \u003cli\u003eMaster the art of data-driven problem-solving with hands-on examples\u003c\/li\u003e \u003cli\u003eFoster your theoretical and practical knowledge of supervised and unsupervised machine learning algorithms\u003c\/li\u003e \u003c\/ul\u003e \u003cp\u003e\u003cstrong\u003eBook Description\u003c\/strong\u003e\u003c\/p\u003e \u003cp\u003eMachine learning is applied everywhere, from business to research and academia, while scikit-learn is a versatile library that is popular among machine learning practitioners. This book serves as a practical guide for anyone looking to provide hands-on machine learning solutions with scikit-learn and Python toolkits.\u003c\/p\u003e \u003cp\u003eThe book begins with an explanation of machine learning concepts and fundamentals, and strikes a balance between theoretical concepts and their applications. Each chapter covers a different set of algorithms, and shows you how to use them to solve real-life problems. You'll also learn about various key supervised and unsupervised machine learning algorithms using practical examples. Whether it is an instance-based learning algorithm, Bayesian estimation, a deep neural network, a tree-based ensemble, or a recommendation system, you'll gain a thorough understanding of its theory and learn when to apply it. As you advance, you'll learn how to deal with unlabeled data and when to use different clustering and anomaly detection algorithms.\u003c\/p\u003e \u003cp\u003eBy the end of this machine learning book, you'll have learned how to take a data-driven approach to provide end-to-end machine learning solutions. You'll also have discovered how to formulate the problem at hand, prepare required data, and evaluate and deploy models in production.\u003c\/p\u003e \u003cp\u003e\u003cstrong\u003eWhat you will learn\u003c\/strong\u003e\u003c\/p\u003e \u003cul\u003e \u003cli\u003eUnderstand when to use supervised, unsupervised, or reinforcement learning algorithms\u003c\/li\u003e \u003cli\u003eFind out how to collect and prepare your data for machine learning tasks\u003c\/li\u003e \u003cli\u003eTackle imbalanced data and optimize your algorithm for a bias or variance tradeoff\u003c\/li\u003e \u003cli\u003eApply supervised and unsupervised algorithms to overcome various machine learning challenges\u003c\/li\u003e \u003cli\u003eEmploy best practices for tuning your algorithm's hyper parameters\u003c\/li\u003e \u003cli\u003eDiscover how to use neural networks for classification and regression\u003c\/li\u003e \u003cli\u003eBuild, evaluate, and deploy your machine learning solutions to production\u003c\/li\u003e \u003c\/ul\u003e \u003cp\u003e\u003cstrong\u003eWho this book is for\u003c\/strong\u003e\u003c\/p\u003e \u003cp\u003eThis book is for data scientists, machine learning practitioners, and anyone who wants to learn how machine learning algorithms work and to build different machine learning models using the Python ecosystem. The book will help you take your knowledge of machine learning to the next level by grasping its ins and outs and tailoring it to your needs. Working knowledge of Python and a basic understanding of underlying mathematical and statistical concepts is required.\u003c\/p\u003e\u003cdiv\u003e\n\u003cstrong\u003eNumber of Pages:\u003c\/strong\u003e 384\u003c\/div\u003e\u003cdiv\u003e\n\u003cstrong\u003eDimensions:\u003c\/strong\u003e 0.79 x 9.25 x 7.5 IN\u003c\/div\u003e\u003cdiv\u003e\n\u003cstrong\u003ePublication Date:\u003c\/strong\u003e July 24, 2020\u003c\/div\u003e","brand":"Books by splitShops","offers":[{"title":"Default Title","offer_id":42743365042239,"sku":"9781838826048","price":76.01,"currency_code":"USD","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0105\/8226\/1823\/files\/759a8dff0dcc39657e0bab3fa058b050.webp?v=1765165454","url":"https:\/\/dhlswag.com\/products\/hands-on-machine-learning-with-scikit-learn-and-scientific-python-toolkits-a-practical-guide-to-implementing-supervised-and-unsupervised-machine-lear-paperback","provider":"BBB","version":"1.0","type":"link"}