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Total Size:
9.5 MB
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FDDAD1462F3DEA91D12C0F254C553DE36D259CD5
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April 16, 2026, 8:57 a.m.
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(Last updated: April 16, 2026, 8:58 a.m.)
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| Code.zip | 2.2 MB |
| Sukup J. scikit-learn Cookbook. Over 80 recipes...in Python...3ed 2025.pdf | 7.3 MB |
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SOURCE: Sukup J. scikit-learn Cookbook. Over 80 recipes...in Python...3ed 2025
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MEDIAINFO
Textbook in PDF format Key benefits 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 Description Trusted 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. *Email sign-up and proof of purchase required Who is this book for? This 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. 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
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