scikit-learn Cookbook: Over 80 recipes for machine learning in Python with scikit-learn

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John Sukup

scikit-learn Cookbook

Over 80 recipes for machine learning in Python with scikit-learn

John Sukup

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English

Packt Publishing Limited
19 December 2025

Electronics engineering; Algorithms & data structures; Machine learning

Get hands-on with the most widely used Python library in machine learning with over 80 practical recipes that cover core as well as advanced functions

Key Features

Solve complex business problems with data-driven approaches Master tools associated with developing predictive and prescriptive models Build robust ML pipelines for real-world applications, avoiding common pitfalls Free with your book: PDF Copy, AI Assistant, and Next-Gen Reader

Book DescriptionTrusted by data scientists, ML engineers, and software developers alike, scikit-learn offers a versatile, user-friendly framework for implementing a wide range of ML algorithms, enabling the efficient development and deployment of predictive models in real-world applications. This third edition of scikit-learn Cookbook will help you master ML with real-world examples and scikit-learn 1.5 features. This updated edition takes you on a journey from understanding the fundamentals of ML and data preprocessing, through implementing advanced algorithms and techniques, to deploying and optimizing ML models in production. Along the way, you’ll explore practical, step-by-step recipes that cover everything from feature engineering and model selection to hyperparameter tuning and model evaluation, all using scikit-learn. By the end of this book, you’ll have gained the knowledge and skills needed to confidently build, evaluate, and deploy sophisticated ML models using scikit-learn, ready to tackle a wide range of data-driven challenges.

What you will learn

Implement a variety of ML algorithms, from basic classifiers to complex ensemble methods, using scikit-learn Perform data preprocessing, feature engineering, and model selection to prepare datasets for optimal model performance Optimize ML models through hyperparameter tuning and cross-validation techniques to improve accuracy and reliability Deploy ML models for scalable, maintainable real-world applications Evaluate and interpret models with advanced metrics and visualizations in scikit-learn Explore comprehensive, hands-on recipes tailored to scikit-learn version 1.5

Who this book is forThis book is for data scientists as well as machine learning and software development professionals looking to deepen their understanding of advanced ML techniques. To get the most out of this book, you should have proficiency in Python programming and familiarity with commonly used ML libraries; e.g., pandas, NumPy, matplotlib, and sciPy. An understanding of basic ML concepts, such as linear regression, decision trees, and model evaluation metrics will be helpful. Familiarity with mathematical concepts such as linear algebra, calculus, and probability will also be invaluable.

By: John Sukup
Imprint: Packt Publishing Limited
Country of Publication: United Kingdom
Edition: 3rd Revised edition
Dimensions: Height: 235mm, Width: 191mm,
ISBN: 9781836644453
ISBN 10: 1836644450
Pages: 388
Publication Date: 19 December 2025
Audience: Professional and scholarly , Undergraduate
Format: Paperback
Publisher's Status: Active

John Sukup is a seventeen-year data professional. His experience working with data spans from consumer market research to data science to ML and AI. He has over a decade of experience as an AI/ML cloud engineer and consultant at multiple international organizations including Levi Strauss, Cisco, Anaconda, and Ipsos. He has acted as the lead professional trainer for Fortune 100 organizations and has been featured in Forbes, Oracle, and Data Science Central. He currently acts as Managing Director and Founder at Expected X, an AI Solution Design and Consultancy as well as cohost of the Unriveted Podcast with his colleague Martin Miller.