AI from Scratch: Step-by-Step Guide to Mastering Artificial Intelligence – Book 5
Unlock the power of machine learning with Scikit-Learn, Python’s most popular ML library! Whether you're a beginner looking to understand the basics or a professional aiming to refine your skills, Mastering Scikit-Learn: Practical ML for Everyone is your ultimate guide to building, optimizing, and deploying machine learning models effectively.
This book is the fifth installment in the AI from Scratch series, designed to provide a structured, hands-on approach to mastering artificial intelligence. With real-world case studies, step-by-step tutorials, and best practices, you’ll gain the confidence to apply machine learning to real business and research problems.
What You’ll Learn:
Part 1: Getting Started with Scikit-Learn
Les informations fournies dans la section « Synopsis » peuvent faire référence à une autre édition de ce titre.
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Paperback. Etat : new. Paperback. AI from Scratch: Step-by-Step Guide to Mastering Artificial Intelligence - Book 5 Unlock the power of machine learning with Scikit-Learn, Python's most popular ML library! Whether you're a beginner looking to understand the basics or a professional aiming to refine your skills, Mastering Scikit-Learn: Practical ML for Everyone is your ultimate guide to building, optimizing, and deploying machine learning models effectively. This book is the fifth installment in the AI from Scratch series, designed to provide a structured, hands-on approach to mastering artificial intelligence. With real-world case studies, step-by-step tutorials, and best practices, you'll gain the confidence to apply machine learning to real business and research problems. What You'll Learn: Part 1: Getting Started with Scikit-LearnIntroduction to machine learning and the Scikit-Learn ecosystemSetting up your Python environment and loading datasetsData preprocessing: handling missing values, feature scaling, and encoding categorical variablesPart 2: Core Machine Learning ModelsImplementing linear regression, logistic regression, and decision treesBuilding powerful ensemble models like Random Forest and Gradient BoostingUnderstanding Support Vector Machines (SVMs) and clustering techniques (K-Means, DBSCAN, PCA)Part 3: Advanced Techniques & OptimizationFeature engineering and recursive feature eliminationHyperparameter tuning with GridSearchCV and Bayesian optimizationHandling imbalanced data, anomaly detection, and data augmentationAutomating ML workflows with Pipelines and AutoMLPart 4: Real-World Applications & DeploymentEnd-to-end machine learning project case studiesIntegrating Scikit-Learn with TensorFlow and PyTorchDeploying ML models using Flask, FastAPI, and cloud platformsAvoiding common pitfalls and optimizing model performanceWho Should Read This Book?Beginners & Students - Learn machine learning from the ground up with hands-on coding examples.Data Scientists & ML Engineers - Deepen your understanding of model tuning and feature engineering.Software Developers - Implement Scikit-Learn models into real-world applications.Business Analysts & AI Enthusiasts - Discover how ML models can drive data-driven decisions.Why Choose This Book?Step-by-Step Learning - Practical examples and code snippets guide you through each concept.Real-World Case Studies - Apply machine learning to real datasets and projects.Hands-on Approach - Learn by doing with interactive exercises and Python implementations.Industry Best Practices - Avoid common pitfalls and optimize your ML models for accuracy and efficiency.Part of the AI from Scratch Series - A structured learning path from beginner to AI mastery.Start Your Machine Learning Journey Today! Whether you're exploring machine learning for the first time or looking to enhance your skills, Mastering Scikit-Learn provides the tools, techniques, and knowledge you need to succeed. Take the next step in your AI journey-Master Scikit-Learn and build powerful machine learning models today! This item is printed on demand. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability. N° de réf. du vendeur 9798309510177
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