{"product_id":"python-machine-learning-machine-learning-and-deep-learning-with-python-scikit-learn-and-tensorflow-2-9781789955750","title":"Python Machine Learning: Machine Learning and Deep Learning with Python, scikit-learn, and TensorFlow 2","description":"\u003cp\u003e\u003cstrong\u003eApplied machine learning with a solid foundation in theory. Revised and expanded for TensorFlow 2, GANs, and reinforcement learning.\u003c\/strong\u003e\u003c\/p\u003e\u003cp\u003e\u003cstrong\u003ePurchase of the print or Kindle book includes a free eBook in the PDF format.\u003c\/strong\u003e\u003c\/p\u003eKey Features\u003cul\u003e\n\u003cli\u003eThird edition of the bestselling, widely acclaimed Python machine learning book\u003c\/li\u003e\n\u003cli\u003eClear and intuitive explanations take you deep into the theory and practice of Python machine learning\u003c\/li\u003e\n\u003cli\u003eFully updated and expanded to cover TensorFlow 2, Generative Adversarial Network models, reinforcement learning, and best practices\u003c\/li\u003e\n\u003c\/ul\u003eBook Description\u003cp\u003ePython Machine Learning, Third Edition is a comprehensive guide to machine learning and deep learning with Python. It acts as both a step-by-step tutorial, and a reference you'll keep coming back to as you build your machine learning systems.\u003c\/p\u003e\u003cp\u003ePacked with clear explanations, visualizations, and working examples, the book covers all the essential machine learning techniques in depth. While some books teach you only to follow instructions, with this machine learning book, Raschka and Mirjalili teach the principles behind machine learning, allowing you to build models and applications for yourself.\u003c\/p\u003e\u003cp\u003eUpdated for TensorFlow 2.0, this new third edition introduces readers to its new Keras API features, as well as the latest additions to scikit-learn. It's also expanded to cover cutting-edge reinforcement learning techniques based on deep learning, as well as an introduction to GANs. Finally, this book also explores a subfield of natural language processing (NLP) called sentiment analysis, helping you learn how to use machine learning algorithms to classify documents.\u003c\/p\u003e\u003cp\u003eThis book is your companion to machine learning with Python, whether you're a Python developer new to machine learning or want to deepen your knowledge of the latest developments.\u003c\/p\u003eWhat you will learn\u003cul\u003e\n\u003cli\u003eMaster the frameworks, models, and techniques that enable machines to 'learn' from data\u003c\/li\u003e\n\u003cli\u003eUse scikit-learn for machine learning and TensorFlow for deep learning\u003c\/li\u003e\n\u003cli\u003eApply machine learning to image classification, sentiment analysis, intelligent web applications, and more\u003c\/li\u003e\n\u003cli\u003eBuild and train neural networks, GANs, and other models\u003c\/li\u003e\n\u003cli\u003eDiscover best practices for evaluating and tuning models\u003c\/li\u003e\n\u003cli\u003ePredict continuous target outcomes using regression analysis\u003c\/li\u003e\n\u003cli\u003eDig deeper into textual and social media data using sentiment analysis\u003c\/li\u003e\n\u003c\/ul\u003eWho this book is for\u003cp\u003eIf you know some Python and you want to use machine learning and deep learning, pick up this book. Whether you want to start from scratch or extend your machine learning knowledge, this is an essential resource. Written for developers and data scientists who want to create practical machine learning and deep learning code, this book is ideal for anyone who wants to teach computers how to learn from data.\u003c\/p\u003eTable of Contents\u003col\u003e\n\u003cli\u003eGiving Computers the Ability to Learn from Data\u003c\/li\u003e\n\u003cli\u003eTraining Simple Machine Learning Algorithms for Classification\u003c\/li\u003e\n\u003cli\u003eA Tour of Machine Learning Classifiers Using scikit-learn\u003c\/li\u003e\n\u003cli\u003eBuilding Good Training Datasets - Data Preprocessing\u003c\/li\u003e\n\u003cli\u003eCompressing Data via Dimensionality Reduction\u003c\/li\u003e\n\u003cli\u003eLearning Best Practices for Model Evaluation and Hyperparameter Tuning\u003c\/li\u003e\n\u003cli\u003eCombining Different Models for Ensemble Learning\u003c\/li\u003e\n\u003cli\u003eApplying Machine Learning to Sentiment Analysis\u003c\/li\u003e\n\u003cli\u003eEmbedding a Machine Learning Model into a Web Application\u003c\/li\u003e\n\u003cli\u003ePredicting Continuous Target Variables with Regression Analysis\u003c\/li\u003e\n\u003cli\u003eWorking with Unlabeled Data - Clustering Analysis\u003c\/li\u003e\n\u003cli\u003eImplementing a Multilayer Artificial Neural Network from Scratch\u003c\/li\u003e\n\u003cli\u003eParallelizing Neural Network Training with TensorFlow\u003c\/li\u003e\n\u003c\/ol\u003e\u003cp\u003e(N.B. Please use the Look Inside option to see further chapters)\u003c\/p\u003e\u003cbr\u003e\u003cbr\u003e\u003cbr\u003e\u003cb\u003eAbout the Author\u003c\/b\u003e\u003cbr\u003e\u003cb\u003e\u003ci\u003eRaschka, Sebastian:\u003c\/i\u003e\u003c\/b\u003e - Sebastian Raschka is an Assistant Professor of Statistics at the University of Wisconsin-Madison focusing on machine learning and deep learning research. Some of his recent research methods have been applied to solving problems in the field of biometrics for imparting privacy to face images. Other research focus areas include the development of methods related to model evaluation in machine learning, deep learning for ordinal targets, and applications of machine learning to computational biology.\u003cb\u003e\u003ci\u003eMirjalili, Vahid:\u003c\/i\u003e\u003c\/b\u003e - Vahid Mirjalili obtained his Ph.D. in mechanical engineering working on novel methods for large-scale, computational simulations of molecular structures. Currently, he is focusing his research efforts on applications of machine learning in various computer vision projects at the Department of Computer Science and Engineering at Michigan State University. He recently joined 3M Company as a research scientist, where he uses his expertise and applies state-of-the-art machine learning and deep learning techniques to solve real-world problems in various applications to make life better.","brand":"Packt Publishing","offers":[{"title":"Default Title","offer_id":50649343918354,"sku":"9781789955750","price":50.99,"currency_code":"USD","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0831\/4771\/8930\/files\/img_aeed4244-5009-44b7-9a8a-565125b1860b.jpg?v=1733261238","url":"https:\/\/surprise-castle.myshopify.com\/products\/python-machine-learning-machine-learning-and-deep-learning-with-python-scikit-learn-and-tensorflow-2-9781789955750","provider":"Surprise Castle","version":"1.0","type":"link"}