Scikit-Learn is a powerful, open-source ML library for Python that provides simple and efficient tools for model development and deployment. Data scientists, ML engineers, and software developers learn Scikit-Learn because it offers a versatile, user-friendly framework for implementing a wide range of ML algorithms, enabling efficient development and deployment of predictive models in real-world applications.

Scikit-learn Cookbook (3rd Edition) takes the reader 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, readers will 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, readers will have the knowledge and skills to confidently build, evaluate, and deploy sophisticated ML models using Scikit-Learn, enabling them to tackle a wide range of data-driven challenges.

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Spis treści

Scikit-learn Cookbook. Over 80 recipes for machine learning in Python with scikit-learn - Third Edition eBook -- spis treści

• 1. Common Conventions and API Elements of Scikit-Learn
• 2. Pre-Model Workflow and Data Preprocessing
• 3. Dimensionality Reduction Techniques
• 4. Building Models with Distance Metrics and Nearest Neighbors
• 5. Linear Models and Regularization
• 6. Advanced Logistic Regression and Extensions
• 7. Support Vector Machines and Kernel Methods
• 8. Tree-Based Algorithms and Ensemble Methods
• 9. Text Processing and Multiclass Classification
• 10. Clustering Techniques
• 11. Novelty and Outlier Detection
• 12. Cross-Validation and Model Evaluation Techniques
• 13. Deploying Scikit-Learn Models in Production