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

Master the most widely used Python library in machine learning with over 'N' practical recipes that cover core and advanced functions. Key Features Solve complex business problems with data-driven approaches Master tools associated with developing predictive/prescriptive models Build robust ML pipelines for real-world applications Avoid common pitfalls in ML pipeline development Learn comprehensive, hands-on recipes tailored to Scikit-Learn version 1.5 Master ML with real-world examples and Scikit-Learn 1.5 features Book DescriptionScikit-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.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 Who this book is forAre you a data scientist, machine learning, or software development professional looking to deepen their understanding of advanced ML techniques? Then this book is for you! To get the most out of this book, you should have a proficiency in Python programming and familiarity with commonly used ML libraries (e.g., pandas, NumPy, matplotlib, sciPy, etc.) Additionally, an understanding of basic ML concepts, like linear regression, decision trees, and model evaluation metrics is helpful. Familiarity with mathematical concepts such as linear algebra, calculus, and probability is also invaluable.

Table of Contents:
Table of Contents Common Conventions and API Elements of Scikit-Learn Pre-Model Workflow and Data Preprocessing Dimensionality Reduction Techniques Building Models with Distance Metrics and Nearest Neighbors Linear Models and Regularization Advanced Logistic Regression and Extensions Support Vector Machines and Kernel Methods Tree-Based Algorithms and Ensemble Methods Text Processing and Multiclass Classification Clustering Techniques Novelty and Outlier Detection Cross-Validation and Model Evaluation Techniques Deploying Scikit-Learn Models in Production

About 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.