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Machine Learning with LightGBM and Python

You're reading from   Machine Learning with LightGBM and Python A practitioner's guide to developing production-ready machine learning systems

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Product type Paperback
Published in Sep 2023
Publisher Packt
ISBN-13 9781800564749
Length 252 pages
Edition 1st Edition
Languages
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Author (1):
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Andrich van Wyk Andrich van Wyk
Author Profile Icon Andrich van Wyk
Andrich van Wyk
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Table of Contents (17) Chapters Close

Preface 1. Part 1: Gradient Boosting and LightGBM Fundamentals
2. Chapter 1: Introducing Machine Learning FREE CHAPTER 3. Chapter 2: Ensemble Learning – Bagging and Boosting 4. Chapter 3: An Overview of LightGBM in Python 5. Chapter 4: Comparing LightGBM, XGBoost, and Deep Learning 6. Part 2: Practical Machine Learning with LightGBM
7. Chapter 5: LightGBM Parameter Optimization with Optuna 8. Chapter 6: Solving Real-World Data Science Problems with LightGBM 9. Chapter 7: AutoML with LightGBM and FLAML 10. Part 3: Production-ready Machine Learning with LightGBM
11. Chapter 8: Machine Learning Pipelines and MLOps with LightGBM 12. Chapter 9: LightGBM MLOps with AWS SageMaker 13. Chapter 10: LightGBM Models with PostgresML 14. Chapter 11: Distributed and GPU-Based Learning with LightGBM 15. Index 16. Other Books You May Enjoy

Preface

Welcome to Machine Learning with LightGBM and Python: A Practitioner’s Guide to Developing Production-Ready Machine Learning Systems. In this book, you’ll embark on a rich journey, taking you from the foundational principles of machine learning to the advanced realms of MLOps. The cornerstone of our exploration is LightGBM, a powerful and flexible gradient-boosting framework that can be harnessed for a wide range of machine-learning challenges.

This book is tailor-made for anyone passionate about transforming raw data into actionable insights using the power of Machine Learning (ML). Whether you’re an ML novice eager to get your hands dirty or an experienced data scientist seeking to master the intricacies of LightGBM, there’s something in here for you.

The digital era has equipped us with a treasure trove of data. However, the challenges often lie in extracting meaningful insights from this data and deploying scalable, efficient, and reliable models in production environments. This book will guide you in overcoming these challenges. By diving into gradient boosting, the data science life cycle, and the nuances of production deployment, you will gain a comprehensive skill set to navigate the ever-evolving landscape of ML.

Each chapter is designed with practicality in mind. Real-world case studies interspersed with theoretical insights ensure your learning is grounded in tangible applications. Our focus on LightGBM, which sometimes gets overshadowed by more mainstream algorithms, provides a unique lens to appreciate and apply gradient boosting in various scenarios.

For those curious about what sets this book apart, it’s our pragmatic approach. We take pride in transcending beyond merely explaining algorithms or tools. Instead, we will prioritize hands-on applications, case studies, and real-world challenges, ensuring you’re not just reading but also “doing” ML.

As we traverse through the chapters, remember that the world of ML is vast and constantly evolving. This book, while comprehensive, is a stepping stone in your lifelong journey of learning and exploration in the domain. As you navigate the world of LightGBM, data science, MLOps, and more, keep your mind open, your curiosity alive, and your hands ready to code.

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