Front Matter
Pages i-xxv
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Understanding Machine Learning
Front Matter
Pages 1-1
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Machine Learning Basics
Dipanjan Sarkar, Raghav Bali, Tushar Sharma
Pages 3-65
The Python Machine Learning Ecosystem
Dipanjan Sarkar, Raghav Bali, Tushar Sharma
Pages 67-118
The Machine Learning Pipeline
Front Matter
Pages 119-119
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Processing, Wrangling, and Visualizing Data
Dipanjan Sarkar, Raghav Bali, Tushar Sharma
Pages 121-176
Feature Engineering and Selection
Dipanjan Sarkar, Raghav Bali, Tushar Sharma
Pages 177-253
Building, Tuning, and Deploying Models
Dipanjan Sarkar, Raghav Bali, Tushar Sharma
Pages 255-304
Real-World Case Studies
Front Matter
Pages 305-305
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Analyzing Bike Sharing Trends
Dipanjan Sarkar, Raghav Bali, Tushar Sharma
Pages 307-330
Analyzing Movie Reviews Sentiment
Dipanjan Sarkar, Raghav Bali, Tushar Sharma
Pages 331-372
Customer Segmentation and Effective Cross Selling
Dipanjan Sarkar, Raghav Bali, Tushar Sharma
Pages 373-405
Analyzing Wine Types and Quality
Dipanjan Sarkar, Raghav Bali, Tushar Sharma
Pages 407-446
Analyzing Music Trends and Recommendations
Dipanjan Sarkar, Raghav Bali, Tushar Sharma
Pages 447-466
Forecasting Stock and Commodity Prices
Dipanjan Sarkar, Raghav Bali, Tushar Sharma
Pages 467-497
Deep Learning for Computer Vision
Dipanjan Sarkar, Raghav Bali, Tushar Sharma
Pages 499-520
Back Matter
Pages 521-530

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.

Practical Machine Learning with Python follows a structured and comprehensive three-tiered approach packed with hands-on examples and code.
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 andframeworks are also covered.

Part 2 details standard machine learning pipelines, with an emphasis on data processing analysis, feature engineering, and modeling. You will learn how to process, wrangle, summarize and visualize data in its various forms. Feature engineering and selection methodologies will be covered in detail with real-world datasets followed by model building, tuning, interpretation and deployment.
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!

What You'll Learn
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.