Mastering Gradient Boosting: A Practical Guide to CatBoost, LightGBM, and XGBoost for Modern Machine Learning

Author:   Dr Benjamin Neudorf
Publisher:   Independently Published
ISBN:  

9798265715036


Pages:   210
Publication Date:   16 September 2025
Format:   Paperback
Availability:   Available To Order   Availability explained
We have confirmation that this item is in stock with the supplier. It will be ordered in for you and dispatched immediately.

Our Price $70.70 Quantity:  
Add to Cart

Share |

Mastering Gradient Boosting: A Practical Guide to CatBoost, LightGBM, and XGBoost for Modern Machine Learning


Overview

Unlock the Power of Modern Machine Learning-No Experience Required Are you fascinated by the buzz around machine learning but feel overwhelmed by the jargon, math, or where to even start? Maybe you've seen words like CatBoost, LightGBM, or XGBoost in tutorials and forums, but every explanation seems written for experts. You're not alone-and you don't need a computer science degree to master these powerful tools. Mastering Gradient Boosting is your friendly, step-by-step guide to conquering three of today's most essential machine learning libraries. Whether you're an absolute beginner or a curious professional, this book welcomes you with open arms-demystifying complex concepts and turning technical obstacles into practical victories. What Makes This Book Different? Instead of intimidating you with formulas or skipping key steps, this book gently guides you from the basics to hands-on mastery: Zero Prerequisites: No advanced math or coding experience required. Every chapter explains terms, breaks down code, and celebrates your progress. Learn by Doing: Build real projects from scratch using Python and today's most in-demand libraries-CatBoost, LightGBM, and XGBoost. Confidence-Building Approach: Each section is designed to reduce anxiety, normalize mistakes, and transform uncertainty into ""aha!"" moments. Complete Practical Coverage: Install and set up your environment with ease Understand gradient boosting, decision trees, and ensemble learning Train, tune, and evaluate powerful models with clear, bite-sized code Explore real-world case studies in finance, healthcare, and customer analytics Interpret results and deploy models for real impact Key Takeaways You'll Gain: Build high-performance ML models for tabular data-even as a beginner Master model evaluation, hyperparameter tuning, and interpretability (SHAP, LIME, etc.) Develop a robust workflow you can use in Kaggle competitions, job interviews, or your own data projects Gain skills trusted by data scientists, analysts, and tech teams worldwide A Supportive Guide for Lifelong Learners Learning machine learning should be empowering-not intimidating. This book meets you where you are, encourages your curiosity, and helps you turn small wins into big breakthroughs. Each chapter ends with tips, encouragement, and next steps, making the journey enjoyable at every turn. Perfect For: Beginners, students, and career-changers Self-learners eager to build job-ready skills Anyone seeking a supportive introduction to CatBoost, LightGBM, and XGBoost Ready to unlock your potential and master the most in-demand machine learning skills? Start your journey with Mastering Gradient Boosting-and see just how far you can go.

Full Product Details

Author:   Dr Benjamin Neudorf
Publisher:   Independently Published
Imprint:   Independently Published
Dimensions:   Width: 17.80cm , Height: 1.10cm , Length: 25.40cm
Weight:   0.372kg
ISBN:  

9798265715036


Pages:   210
Publication Date:   16 September 2025
Audience:   General/trade ,  General
Format:   Paperback
Publisher's Status:   Active
Availability:   Available To Order   Availability explained
We have confirmation that this item is in stock with the supplier. It will be ordered in for you and dispatched immediately.

Table of Contents

Reviews

Author Information

Tab Content 6

Author Website:  

Countries Available

All regions
Latest Reading Guide

RGFEB26

 

Shopping Cart
Your cart is empty
Shopping cart
Mailing List