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资源说明：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 and frameworks 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.
Who This Book Is For
IT professionals, analysts, developers, data scientists, engineers, graduate students
Table of Contents
Part I: Understanding Machine Learning
Chapter 1: Machine Learning Basics
Chapter 2: The Python Machine Learning Ecosystem
Part II: The Machine Learning Pipeline
Chapter 3: Processing, Wrangling, and Visualizing Data
Chapter 4: Feature Engineering and Selection
Chapter 5: Building, Tuning, and Deploying Models
Part III: Real-World Case Studies
Chapter 6: Analyzing Bike Sharing Trends
Chapter 7: Analyzing Movie Reviews Sentiment
Chapter 8: Customer Segmentation and Effective Cross Selling
Chapter 9: Analyzing Wine Types and Quality
Chapter 10: Analyzing Music Trends and Recommendations
Chapter 11: Forecasting Stock and Commodity Prices
Chapter 12: Deep Learning for Computer Vision
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