Data is the new oil and Machine Learning is a powerful concept and framework for making the best out of it. In this age of automation and intelligent systems, it is hardly a surprise that Machine Learning and Data Science are some of the top buzz words. The tremendous interest and renewed investments in the field of Data Science across industries, enterprises, and domains are clear indicators of its enormous potential. Intelligent systems and data-driven organizations are becoming a reality and the advancements in tools and techniques is only helping it expand further. With data being of paramount importance, there has never been a higher demand for Machine Learning and Data Science practitioners than there is now. Indeed, the world is facing a shortage of data scientists. It’s been coined “The sexiest job in the 21st Century” which makes it all the more worthwhile to try to build some valuable expertise in this domain.
Practical Machine Learning with Python is a problem solver’s guide to building real-world intelligent systems. It follows a comprehensive three-tiered approach packed with concepts, methodologies, hands-on examples, and code. This book helps its readers master the essential skills needed to recognize and solve complex problems with Machine Learning and Deep Learning by following a data-driven mindset. Using real-world case studies that leverage the popular Python Machine Learning ecosystem, this book is your perfect companion for learning the art and science of Machine Learning to become a successful practitioner. The concepts, techniques, tools, frameworks, and methodologies used in this book will teach you how to think, design, build, and execute Machine Learning systems and projects successfully.
This book will get you started on the ways to leverage the Python Machine Learning ecosystem with its diverse set of frameworks and libraries. The three-tiered approach of this book starts by focusing on building a strong foundation around the basics of Machine Learning and relevant tools and frameworks, the next part emphasizes the core processes around building Machine Learning pipelines, and the final part leverages this knowledge on solving some real-world case studies from diverse domains, including retail, transportation, movies, music, computer vision, art, and finance. We also cover a wide range of Machine Learning models, including regression, classification, forecasting, rule-mining, and clustering. This book also touches on cutting edge methodologies and research from the field of Deep Learning, including concepts like transfer learning and case studies relevant to computer vision, including image classification and neural style transfer. Each chapter consists of detailed concepts with complete hands-on examples, code, and detailed discussions. The main intent of this book is to give a wide range of readers—including IT professionals, analysts, developers, data scientists, engineers, and graduate students—a structured approach to gaining essential skills pertaining to Machine Learning and enough knowledge about leveraging state-of-the-art Machine Learning techniques and frameworks so that they can start solving their own real-world problems. This book is application-focused, so it’s not a replacement for gaining deep conceptual and theoretical knowledge about Machine Learning algorithms, methods, and their internal implementations. We strongly recommend you supplement the practical knowledge gained through this book with some standard books on data mining, statistical analysis, and theoretical aspects of Machine Learning algorithms and methods to gain deeper insights into the world of Machine Learning.
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