scikit-learn Cookbook

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

Free with your DRM-free PDF version + access to Packt’s next-gen Reader*

Key FeaturesSolve complex business problems with data-driven approachesMaster tools associated with developing predictive and prescriptive modelsBuild robust ML pipelines for real-world applications, avoiding common pitfallsFree with your PDF Copy, AI Assistant, and Next-Gen ReaderBook 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.

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What you will learnImplement a variety of ML algorithms, from basic classifiers to complex ensemble methods, using scikit-learnPerform data preprocessing, feature engineering, and model selection to prepare datasets for optimal model performanceOptimize ML models through hyperparameter tuning and cross-validation techniques to improve accuracy and reliabilityDeploy ML models for scalable, maintainable real-world applicationsEvaluate and interpret models with advanced metrics and visualizations in scikit-learnExplore comprehensive, hands-on recipes tailored to scikit-learn version 1.5Who 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.

Table of ContentsCommon Conventions and API Elements of scikit-learnPre-Model Workflow and Data PreprocessingDimensionality Reduction TechniquesBuilding Models with Distance Metrics and Nearest NeighborsLinear Models and RegularizationAdvanced Logistic Regression and ExtensionsSupport Vector Machines and Kernel MethodsTree-Based Algorithms and Ensemble MethodsText Processing and Multiclass ClassificationClustering Techniques

Size: A4(20*28cm)

Printing: 80 gm – color

Cover: Softcover