What this book covers

Chapter 1, Common Conventions and API Elements of scikit-learn, covers the standard conventions and core API elements of scikit-learn, including the design principles behind estimators, transformers, and pipelines, as well as common methods such as fit(), predict(), and transform().

Chapter 2, Pre-Model Workflow and Data Preprocessing, covers preprocessing tools and techniques, including enhanced data transformers and feature engineering methods.

Chapter 3, Dimensionality Reduction Techniques, includes updated approaches for dimensionality reduction with new algorithms and improvements in scikit-learn.

Chapter 4, Building Models with Distance Metrics and Nearest Neighbors, includes updates on the latest developments in distance metric-based models.

Chapter 5, Linear Models and Regularization, covers the linear models and regularization techniques that are now available.

Chapter 6, Advanced Logistic Regression and Extensions, explores the latest advancements in logistic regression and its extensions.

Chapter 7, Support Vector Machines and Kernel Methods, covers features and optimizations in SVMs and kernel methods.

Chapter 8, Tree-Based Algorithms and Ensemble Methods, includes the latest improvements and new ensemble techniques.

Chapter 9, Text Processing and Multiclass Classification, covers new text vectorization methods and multiclass classification strategies.

Chapter 10, Clustering Techniques, explores unsupervised learning techniques for finding naturally occurring groupings of similar data points.

Chapter 11, Novelty and Outlier Detection, covers techniques for finding inlier and outlier data points in training datasets.

Chapter 12, Cross-Validation and Model Evaluation Techniques, covers cross-validation strategies, scoring methods, and model evaluation tools.

Chapter 13, Deploying scikit-learn Models in Production, includes tools and best practices for deploying scikit-learn models in production environments, with a focus on scalability and maintainability.