Python machine learning: machine learning and deep learning with Python, scikit-learn, and TensorFlow
Key Features A practical approach to the frameworks of data science, machine learning, and deep learningUse the most powerful Python libraries to implement machine learning and deep learningLearn best practices to improve and optimize your machine learning systems and algorithms Book Description Machine learning is eating the software world, and now deep learning is extending machine learning. This book is for developers and data scientists who want to master the world of artificial intelligenc…
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- Full title :Python machine learning : machine learning and deep learning with Python, scikit-learn, and TensorFlow / Sebastian Raschka, Vahid Mirjalili.
Summary
Key Features A practical approach to the frameworks of data science, machine learning, and deep learningUse the most powerful Python libraries to implement machine learning and deep learningLearn best practices to improve and optimize your machine learning systems and algorithms Book Description Machine learning is eating the software world, and now deep learning is extending machine learning. This book is for developers and data scientists who want to master the world of artificial intelligence, with a practical approach to understanding and implementing machine learning, and how to apply the power of deep learning with Python. This Second Edition of Sebastian Raschka's Python Machine Learning is thoroughly updated to use the most powerful and modern Python open-source libraries, so that you can understand and work at the cutting-edge of machine learning, neural networks, and deep learning. Written for developers and data scientists who want to create practical machine learning code, the authors have extended and modernized this best-selling book, to now include the influential TensorFlow library, and the Keras Python neural network library. The Scikit-learn code has also been fully updated to include recent innovations. The result is a new edition of this classic book at the cutting edge of machine learning. Readers new to machine learning will find this classic book offers the practical knowledge and rich techniques they need to create and contribute to machine learning, deep learning, and modern data analysis. Raschka and Mirjalili introduce you to machine learning and deep learning algorithms, and show you how to apply them to practical industry challenges. By the end of the book, you'll be ready to meet the new data analysis opportunities in today's world. Readers of the first edition will be delighted to find a new balance of classical ideas and modern insights into machine learning. Every chapter has been critically updated, and there are new chapters on key technologies. Readers can learn and work with TensorFlow more deeply than ever before, and essential coverage of the Keras neural network library has been added, along with the most recent updates to Scikit-learn. Raschka and Mirjalili have updated this book to meet the most modern areas of machine learning, to give developers and data scientists a fresh and practical Python journey into machine learning. What you will learn Use the key frameworks of data science, machine learning, and deep learningAsk new questions of your data through machine learning models and neural networksWork with the most powerful Python open-source libraries in machine learningBuild deep learning applications using Keras and TensorFlowEmbed your machine learning model in accessible web applicationsPredict continuous target outcomes using regression analysisUncover hidden patterns and structures in data with clusteringAnalyze images using deep learning techniquesUse sentiment analysis to delve deeper into textual and social media data About the Author Sebastian Raschka, author of the best selling Python Machine Learning, has many years of experience with coding in Python and has given several seminars on the practical applications of data science and machine learning, including a machine learning tutorial at SciPy, the leading conference for scientific computing in Python. Sebastian loves to write and talk about data science, machine learning, and Python, and he's motivated to help people developing data-driven solutions without necessarily requiring a machine learning background. His work and contributions have recently been recognized by the departmental outstanding graduate student award 2016-2017. In his free time, Sebastian loves to contribute to open source projects, and methods that he implemented are now successfully used in machine learning competitions such as Kaggle. Vahid Mirjalili obtained his Ph.D. in mechanical engineering working on nove.
Read More ↓Edition Statements
- Publication Date:© 2017
- Edition:2. edition, fully revised and updated
Publication Statements
- Publisher(s):Packt Publishing
- Place of Publication:Birmingham ; Mumbai
- Language:eng
- Publication Date:© 2017
The Physical Object
- Description:xviii, 595 Seiten : : Illustrationen, Diagramme
Edition Identifiers
- ISBN:9781787125933, 1787125939
- oclc:on1011364509
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