Applied Machine Learning Made Practical: A Hands-On Guide to Data Preparation, Algorithm Selection,Model Building,and Real-World Machine Learning. - Softcover

Vale, Ethan

Softcover

ISBN 13: 9798253785720

Publisher: Independently published, 2026

Synopsis

Machine learning doesn’t have to be complicated or theoretical.

Applied Machine Learning Made Practical: A Hands-On Guide to Data Preparation, Algorithm Selection, Model Building, and Real-World Machine Learning is your step-by-step roadmap to actually building and applying machine learning solutions that work.

Instead of overwhelming you with abstract theory, this book focuses on what really matters—practical skills you can use immediately.

Whether you're a beginner, student, developer, or aspiring data scientist, this guide helps you understand the full machine learning workflow from start to finish.

Inside this book, you’ll learn how to:

Prepare and clean real-world datasets effectively

Understand and select the right machine learning algorithms

Build, train, and evaluate models with confidence

Avoid common mistakes and improve model performance

Work with classification, regression, and clustering techniques

Handle overfitting, underfitting, and bias

Tune models for better accuracy and reliability

Deploy machine learning solutions in real-world scenarios

Turn data into actionable insights for business and decision-making

This book bridges the gap between theory and practice—giving you the tools, mindset, and confidence to solve real problems using machine learning.

If you’re ready to move beyond tutorials and start building real-world models, this guide will show you exactly how.

Disclaimer

This book is intended for educational and informational purposes only. It is an independent publication and is not affiliated with or endorsed by any specific organization or platform. All tools, technologies, and libraries mentioned are the property of their respective owners. The author assumes no responsibility for errors, omissions, or outcomes resulting from the use of this material.

Edition details

• Publisher: Independently published
• Publication date: 2026
• Language: English
• ISBN 13: 9798253785720
• Binding: Paperback
• Number of pages: 285