A complete guide of theoretical, technical, and hands-on implementations for practical applications of machine learning across diverse domains in the industry
Shows how data science and machine learning projects are executed in the real world
Provides readers with the essential skills to tackle their own real-world problems with machine learning
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. Part 2 details standard machine learning pipelines, with an emphasis on data processing analysis, feature engineering, and modeling. Part 3 explores multiple real-world casestudies spanning diverse domains and industries like retail, transportation, movies, music, marketing, computer vision and finance.
Practical Machine Learning with Python
will empower you to start solving your own problems with machine learning today!
You will:
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Execute end-to-end machine learning projects and systems
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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.
