scikit-learn Cookbook, Third Edition

This is the code repository for scikit-learn Cookbook, Third Edition, published by Packt.

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

John Sukup

                 

About the book

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.

Key Learnings

• 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

Chapters

Chapters	Colab	Kaggle	Gradient	Studio Lab
Chapter 1: Common Conventions and API Elements of Scikit-Learn
Chapter 2: Pre-Model Workflow and Data Preprocessing
Chapter 2 Pre Model Workflow and Data Preprocessing (Exercise Solution).ipynb
Chapter 2 Pre Model Workflow and Data Preprocessing.ipynb
Chapter 3: Dimensionality Reduction Techniques
Chapter 3 Dimensionality Reduction Techniques (Exercise Solution).ipynb
Chapter 3 Dimensionality Reduction Techniques.ipynb
Chapter 4: Building Models with Distance Metrics and Nearest Neighbors
Chapter 4 Building Models with Distance Metrics and Nearest Neighbors (Exercise Solution).ipynb
Chapter 4 Building Models with Distance Metrics and Nearest Neighbors.ipynb
Chapter 5: Linear Models and Regularization
Chapter 5 Linear Models and Regularization (Exercise Solution).ipynb
Chapter 5 Linear Models and Regularization.ipynb
Chapter 6: Advanced Logistic Regression and Extensions
Chapter 6 Advanced Logistic Regression and Extensions (Exercise Solutions).ipynb
Chapter 6 Advanced Logistic Regression and Extensions.ipynb
Chapter 7: Support Vector Machines and Kernel Methods
Chapter 7 Support Vector Machines and Kernel Methods (Exercise Solutions).ipynb
Chapter 7 Support Vector Machines and Kernel Methods.ipynb
Chapter 8: Tree-Based Algorithms and Ensemble Methods
Chapter 8 Tree-Based Algorithms and Ensemble Methods (Exercise Solutions).ipynb
Chapter 8 Tree-Based Algorithms and Ensemble Methods.ipynb
Chapter 9: Text Processing and Multiclass Classification
Chapter 9 Text Processing and Multiclass Classification (Exercise Solutions).ipynb
Chapter 9 Text Processing and Multiclass Classification.ipynb
Chapter 10: Clustering Techniques
Chapter 10 Clustering Techniques (Exercise Solutions).ipynb
Chapter 10 Clustering Techniques.ipynb
Chapter 11: Novelty and Outlier Detection
Chapter 11 Outlier and Novelty Detection (Exercise Solutions).ipynb
Chapter 11 Outlier and Novelty Detection.ipynb
Chapter 12: Cross-Validation and Model Evaluation Techniques
Chapter 12 Cross-Validation and Model Evaluation Techniques (Exercise Solutions).ipynb
Chapter 12 Cross-Validation and Model Evaluation Techniques.ipynb
Chapter 13: Deploying Scikit-Learn Models in Production
Chapter 13 Deploying scikit-learn Models in Production (Exercise Solutions).ipynb
Chapter 13 Deploying scikit-learn Models in Production.ipynb

Requirements for this book

This book is designed to provide basic examples of the most important features of scikit-learn v1.5. In order to maximize the effectiveness of your learning, in addition to completing the exercises in each chapter, we encourage you to try your own examples and explore additional function arguments beyond those presented. Additionally, combining your learning from different chapters is an effective way to coalesce your scikit-learning understanding holistically.

Software/hardware covered in the book	OS requirements
scikit-learn v1.5 or greater	Windows, macOS X, and Linux (any)
Git ≥ 2.46.x	Windows, macOS X, and Linux (any)
Python ≥ 3.9.x	Windows, macOS X, and Linux (any)

Get to know the Author

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.

Other Related Books

• Python Data Cleaning Cookbook, Second Edition
• Python Machine Learning, Third Edition