Applied Machine Learning with Scikit-Learn: Transform Your Data Into Predictive Models and Build End-to-End AI Solutions for Modern Web Apps

Kuester, Max

ISBN 13: 9798275717419

Edité par Independently published, 2025

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N° de réf. du vendeur I-9798275717419

Détails bibliographiques

• Titre: Applied Machine Learning with Scikit-Learn: Transform Your Data Into Predictive Models and Build End-to-End AI Solutions for Modern Web Apps
• Éditeur: Independently published
• Date de parution: 2025
• Langue: anglais
• ISBN à 13 chiffres: 9798275717419
• Reliure: Couverture souple
• État de l'article: New

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Synopsis

What if you could understand machine-learning results with complete confidence—without getting lost in complicated math or confusing explanations?
This book gives you that clarity.
BOOK TITLES delivers a practical, beginner-friendly path to mastering real-world machine learning using Scikit-Learn, the most trusted toolkit in Python. At its core, this book solves a single problem: helping you move from “I understand the idea” to “I can actually build and evaluate models that work.” Every chapter builds skill, accuracy, and confidence—without overwhelming theory.
You’ll quickly learn how to structure data, choose the right algorithm, train models efficiently, and evaluate them with meaningful metrics. Through clear explanations and hands-on examples, you’ll understand why models behave the way they do and how to improve them with smarter preprocessing, tuning, and validation techniques.

You’ll be able to:

• Build classification, regression, and clustering models that produce reliable results.

• Apply essential preprocessing steps such as scaling, encoding, and feature selection.

• Evaluate models using precision, recall, F1-score, confusion matrices, and cross-validation.

• Strengthen your models through hyperparameter tuning, pipelines, and proper train/test practices.

• Work effectively with real datasets and interpret outcomes with confidence.

• Understand advanced topics such as ensemble methods, dimensionality reduction, and model optimization—explained in clear, actionable language.

From foundational principles to practical implementation, each chapter offers direct benefits: better models, stronger intuition, and the ability to turn raw data into useful predictions.

Whether you’re a student, a beginner stepping into machine learning, or a developer expanding your skill set, this book gives you the tools to create accurate, well-tested models using Python’s most accessible and powerful library.

If you’re ready to build real machine-learning solutions with confidence, clarity, and accuracy, get your copy of BOOK TITLES today.