Isbn: 9798258629869 - practical machine learning projects with python: build, train, and deploy real-world models using scikit-learn and modern data science workflows: 3 (the practical ai & machine learning series) (9 resultados)

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PAP. Condición: New. New Book. Shipped from UK. Established seller since 2000.

PAP. Condición: New. New Book. Shipped from UK. Established seller since 2000.

Condición: New.

Condición: As New. Unread book in perfect condition.

Paperback. Condición: new. Paperback. Build real-world machine learning systems not just models.Practical Machine Learning Projects with Python is a hands-on, project-driven guide designed to take you from theory to deployment. Instead of abstract concepts, you'll build complete ML solutions from data preprocessing and feature engineering to model training, evaluation, API deployment, and production monitoring.Inside, you'll learn how to: Build and optimize regression and classification models using Scikit-LearnTackle real-world problems like churn prediction, fraud detection, and credit riskStructure end-to-end ML workflows used in industryDeploy models as APIs and monitor them in productionAvoid common pitfalls like data leakage, overfitting, and poor evaluationThis book is built for beginners to intermediate practitioners who want practical, job-ready skills not just theory.Why choose this book?Project-based learning with real datasets and business contextClear, step-by-step Python implementationsCovers the full lifecycle: from idea model deployment maintenanceDesigned to help you build a strong portfolio and real-world confidenceIf you're ready to stop watching tutorials and start building production-ready machine learning system. This item is printed on demand. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.…

Condición: New. Print on Demand.

Paperback. Condición: new. Paperback. Build real-world machine learning systems not just models.Practical Machine Learning Projects with Python is a hands-on, project-driven guide designed to take you from theory to deployment. Instead of abstract concepts, you'll build complete ML solutions from data preprocessing and feature engineering to model training, evaluation, API deployment, and production monitoring.Inside, you'll learn how to: Build and optimize regression and classification models using Scikit-LearnTackle real-world problems like churn prediction, fraud detection, and credit riskStructure end-to-end ML workflows used in industryDeploy models as APIs and monitor them in productionAvoid common pitfalls like data leakage, overfitting, and poor evaluationThis book is built for beginners to intermediate practitioners who want practical, job-ready skills not just theory.Why choose this book?Project-based learning with real datasets and business contextClear, step-by-step Python implementationsCovers the full lifecycle: from idea model deployment maintenanceDesigned to help you build a strong portfolio and real-world confidenceIf you're ready to stop watching tutorials and start building production-ready machine learning system. This item is printed on demand. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability.…