Python Machine Learning Cookbook, - Info and Reading Options

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bDiscover powerful ways to effectively solve real-world machine learning problems using key libraries including scikit-learn, TensorFlow, and PyTorch/b h4Key Features/h4 ulliLearn and implement machine learning algorithms in a variety of real-life scenarios /li liCover a range of tasks catering to supervised, unsupervised and reinforcement learning techniques /li liFind easy-to-follow code solutions for tackling common and not-so-common challenges /li /ul h4Book Description/h4 This eagerly anticipated second edition of the popular Python Machine Learning Cookbook will enable you to adopt a fresh approach to dealing with real-world machine learning and deep learning tasks. With the help of over 100 recipes, you will learn to build powerful machine learning applications using modern libraries from the Python ecosystem. The book will also guide you on how to implement various machine learning algorithms for classification, clustering, and recommendation engines, using a recipe-based approach. With emphasis on practical solutions, dedicated sections in the book will help you to apply supervised and unsupervised learning techniques to real-world problems. Toward the concluding chapters, you will get to grips with recipes that teach you advanced techniques including reinforcement learning, deep neural networks, and automated machine learning. By the end of this book, you will be equipped with the skills you need to apply machine learning techniques and leverage the full capabilities of the Python ecosystem through real-world examples. h4What you will learn/h4 ulliUse predictive modeling and apply it to real-world problems /li liExplore data visualization techniques to interact with your data /li liLearn how to build a recommendation engine /li liUnderstand how to interact with text data and build models to analyze it /li liWork with speech data and recognize spoken words using Hidden Markov Models /li liGet well versed with reinforcement learning, automated ML, and transfer learning /li liWork with image data and build systems for image recognition and biometric face recognition /li liUse deep neural networks to build an optical character recognition system/li/ul h4Who this book is for/h4 This book is for data scientists, machine learning developers, deep learning enthusiasts and Python programmers who want to solve real-world challenges using machine-learning techniques and algorithms. If you are facing challenges at work and want ready-to-use code solutions to cover key tasks in machine learning and the deep learning domain, then this book is what you need. Familiarity with Python programming and machine learning concepts will be useful

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