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{ "item_title" : "Applied Machine Learning with Scikit-Learn", "item_author" : [" Max Kuester "], "item_description" : "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.", "item_img_path" : "https://covers4.booksamillion.com/covers/bam/9/79/827/571/9798275717419_b.jpg", "price_data" : { "retail_price" : "39.66", "online_price" : "39.66", "our_price" : "39.66", "club_price" : "39.66", "savings_pct" : "0", "savings_amt" : "0.00", "club_savings_pct" : "0", "club_savings_amt" : "0.00", "discount_pct" : "10", "store_price" : "" } }
Applied Machine Learning with Scikit-Learn|Max Kuester

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

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Overview

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.

This item is Non-Returnable

Details

  • ISBN-13: 9798275717419
  • ISBN-10: 9798275717419
  • Publisher: Independently Published
  • Publish Date: November 2025
  • Dimensions: 10 x 7 x 0.79 inches
  • Shipping Weight: 1.46 pounds
  • Page Count: 384

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