Master Machine Learning with scikit-learn (Paperback)

Kevin Markham

ISBN 13: 9798299179460

Published by Independently Published, 2026

About this Item

Paperback. This is a practical guide to help you transform from Machine Learning novice to skilled Machine Learning practitioner. Throughout the book, you'll learn the best practices for proper Machine Learning and how to apply those practices to your own Machine Learning problems. By the end of this book, you'll be more confident when tackling new Machine Learning problems because you'll understand what steps you need to take, why you need to take them, and how to correctly execute those steps using scikit-learn. You'll know what problems you might run into, and you'll know exactly how to solve them. Because you're learning a better way to work in scikit-learn, your code will be easier to write and to read, and you'll get better Machine Learning results faster than before! "If you think that Machine Learning is too complex for you to learn, I cannot recommend this book enough. It will give you the confidence you need, along with the knowledge you want."- Reuven Lerner, Python trainer "By far the best book I've read on scikit-learn. The later chapters, in particular, helped me significantly deepen my understanding and improve my use of the library."- Patrick Ryan, Software Engineer "Exceptionally well-structured and easy to grasp."- Marco Peters, Business Intelligence Analyst Kevin Markham is the founder of Data School, an online school for learning Data Science with Python. He has been teaching Machine Learning in the classroom and online for more than 10 years, and is passionate about teaching people who are new to the field. He has a degree in Computer Engineering from Vanderbilt University and lives in Asheville, North Carolina. Topics covered: Review of the basic Machine Learning workflowEncoding categorical featuresEncoding text dataHandling missing valuesPreparing complex datasetsCreating an efficient workflow for preprocessing and model buildingTuning your workflow for maximum performanceAvoiding data leakageProper model evaluationAutomatic feature selectionFeature standardizationFeature engineering using custom transformersLinear and non-linear modelsModel ensemblingModel persistenceHandling high-cardinality categorical featuresHandling class imbalance This item is printed on demand. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.

Seller Inventory # 9798299179460

Bibliographic Details

• Title: Master Machine Learning with scikit-learn (Paperback)
• Publisher: Independently Published
• Publication Date: 2026
• Language: English
• ISBN 13: 9798299179460
• Binding: Paperback
• Condition: new

About this title

Synopsis

This is a practical guide to help you transform from Machine Learning novice to skilled Machine Learning practitioner.

Throughout the book, you’ll learn the best practices for proper Machine Learning and how to apply those practices to your own Machine Learning problems. By the end of this book, you’ll be more confident when tackling new Machine Learning problems because you’ll understand what steps you need to take, why you need to take them, and how to correctly execute those steps using scikit-learn. You’ll know what problems you might run into, and you’ll know exactly how to solve them. Because you’re learning a better way to work in scikit-learn, your code will be easier to write and to read, and you’ll get better Machine Learning results faster than before!

"If you think that Machine Learning is too complex for you to learn, I cannot recommend this book enough. It will give you the confidence you need, along with the knowledge you want."
- Reuven Lerner, Python trainer

"By far the best book I've read on scikit-learn. The later chapters, in particular, helped me significantly deepen my understanding and improve my use of the library."
- Patrick Ryan, Software Engineer

"Exceptionally well-structured and easy to grasp."
- Marco Peters, Business Intelligence Analyst

Kevin Markham is the founder of Data School, an online school for learning Data Science with Python. He has been teaching Machine Learning in the classroom and online for more than 10 years, and is passionate about teaching people who are new to the field. He has a degree in Computer Engineering from Vanderbilt University and lives in Asheville, North Carolina.

Topics covered:

• Review of the basic Machine Learning workflow
• Encoding categorical features
• Encoding text data
• Handling missing values
• Preparing complex datasets
• Creating an efficient workflow for preprocessing and model building
• Tuning your workflow for maximum performance
• Avoiding data leakage
• Proper model evaluation
• Automatic feature selection
• Feature standardization
• Feature engineering using custom transformers
• Linear and non-linear models
• Model ensembling
• Model persistence
• Handling high-cardinality categorical features
• Handling class imbalance