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Practical Machine Learning with Python: A Problem-Solver's Guide to Building Real-World Intelligent Systems
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.
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.
530 pages, Kindle Edition
Published April 17, 2018
11 people are currently reading
26 people want to read
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June 19, 2020We are trying to implement a data lake on our network. During this work to launch Spark as a processing layer the book gave me a lot of ideas to do. I changed the examples to adapt to execute on cluster then I submitted on our cluster.
January 23, 2024
The book is ok to get some level of overview of some machine learning practices. It doesn't really explain much (mathematical) background of what's going on, so if you don't have that background already you will have a harder time in places. The examples feel a little random, but on the other hand they cover a fairly broad spectrum of application areas which is good. Probably the most important issue with the book nowadays is that it was written in 2017, and in the fast evolving landscape of ML, especially deep learning model architecture, it often feels woefully out of touch with topics of interest today. Also, a lot of the Jupyter notebooks don't run anymore since the software APIs of the current versions of tools used in them have made changes that are not backed compatible. It would have helped if the authors had provided a docker image (or similar) with frozen versions of all the Python libraries that the Jupyter notebooks depend on.
February 25, 2023
This book presents random examples of machine learning exercises. Unfortunately this is no introduction to machine learning, no guide on ML ecosystem in python
Displaying 1 - 3 of 3 reviews