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
General
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