Practical Machine Learning With Python A Problemsolvers Guide To Building Realworld Intelligent Systems Dipanjan Sarkar by Dipanjan Sarkar, Raghav Bali, Tushar Sharma 9781484232064, 1484232062 instant download after payment.

Master the essential skills needed to recognize and solve complex problems with machine learning and deep learning. Using real-world examples 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.

Part 1 focuses on understanding machine learning concepts and tools. This includes machine learning basics with a broad overview of algorithms, techniques, concepts and applications, followed by a tour of the entire Python machine learning ecosystem. Brief guides for useful machine learning tools, libraries and frameworks are also covered.

Part 3 explores multiple real-world case studies spanning diverse domains and industries like retail, transportation, movies, music, marketing, computer vision and finance. For each case study, you will learn the application of various machine learning techniques and methods. The hands-on examples will help you become familiar with state-of-the-art machine learning tools and techniques and understand what algorithms are best suited for any problem.

Practical Machine Learning with Python will empower you to start solving your own problems with machine learning today!

• Execute end-to-end machine learning projects and systems
  
• Implement hands-on examples with industry standard, open source, robust machine learning tools and frameworks
  
• Review case studies depicting applications of machine learning and deep learning on diverse domains and industries
• Apply a wide range of machine learning models including regression, classification, and clustering.
• Understand and apply the latest models and methodologies from deep learning including CNNs, RNNs, LSTMs and transfer learning.