Hands-On Gradient Boosting with Python

Adrian Devlin

ISBN 13: 9798278284963

Published by Amazon Digital Services LLC - Kdp, 2025

About this Item

New Book. Shipped from UK. THIS BOOK IS PRINTED ON DEMAND. Established seller since 2000.

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Bibliographic Details

• Title: Hands-On Gradient Boosting with Python
• Publisher: Amazon Digital Services LLC - Kdp
• Publication Date: 2025
• Language: English
• ISBN 13: 9798278284963
• Binding: PAP
• Condition: New
• Weight: 395 grams

About this title

Synopsis

Are you curious about machine learning but feel overwhelmed by math, jargon, and complex tutorials?
If words like XGBoost, LightGBM, and gradient boosting sound exciting but intimidating, this book is your friendly guide through the noise.

Hands-On Gradient Boosting with Python: A Practical Introduction to XGBoost, LightGBM, and the Scikit-Learn Ecosystem is written for complete beginners and self-taught developers who want a clear, step-by-step path into modern Python machine learning—without needing a PhD or years of coding experience.

You’ll start with the basics of Python, scikit-learn, and tabular data, then gently build up to powerful boosting models used in real-world projects and Kaggle competitions. Every chapter walks you through code line by line, explains why each step matters, and shows you how to avoid common mistakes.

Inside, you’ll learn how to:

• Set up your Python machine learning environment with confidence

• Understand core concepts like decision trees, ensembles, and gradient boosting in plain English

• Build practical models with scikit-learn, XGBoost, and LightGBM for regression and classification

• Work on real-world projects such as house price prediction and credit risk scoring

• Tune hyperparameters, handle imbalanced data, and evaluate models with metrics like AUC, F1, and RMSE

• Use SHAP and LIME for model explainability so you can trust your predictions

• Save, load, and deploy your models so they are ready for real applications

Throughout the book, you’re treated like a learner—not a walking error message. Mistakes are normalized, experiments are encouraged, and every “small win” is celebrated:

• Clear explanations before any code

• Gradual progression from simple to advanced models

• Gentle reminders that confusion is part of learning

• Practical tips for debugging, improving, and reusing your work

Whether you’re a student, an aspiring data scientist, or a developer stepping into Python machine learning for the first time, this book becomes your supportive companion—one that makes gradient boosting feel approachable, understandable, and genuinely fun.

If you’re ready to stop scrolling tutorials and start building real models that actually work, open this book and begin your hands-on journey into gradient boosting with Python today.