Book Image

Machine Learning with LightGBM and Python

By : Andrich van Wyk
Book Image

Machine Learning with LightGBM and Python

By: Andrich van Wyk

Overview of this book

Machine Learning with LightGBM and Python is a comprehensive guide to learning the basics of machine learning and progressing to building scalable machine learning systems that are ready for release. This book will get you acquainted with the high-performance gradient-boosting LightGBM framework and show you how it can be used to solve various machine-learning problems to produce highly accurate, robust, and predictive solutions. Starting with simple machine learning models in scikit-learn, you’ll explore the intricacies of gradient boosting machines and LightGBM. You’ll be guided through various case studies to better understand the data science processes and learn how to practically apply your skills to real-world problems. As you progress, you’ll elevate your software engineering skills by learning how to build and integrate scalable machine-learning pipelines to process data, train models, and deploy them to serve secure APIs using Python tools such as FastAPI. By the end of this book, you’ll be well equipped to use various -of-the-art tools that will help you build production-ready systems, including FLAML for AutoML, PostgresML for operating ML pipelines using Postgres, high-performance distributed training and serving via Dask, and creating and running models in the Cloud with AWS Sagemaker.
Table of Contents (17 chapters)
Part 1: Gradient Boosting and LightGBM Fundamentals
Part 2: Practical Machine Learning with LightGBM
Part 3: Production-ready Machine Learning with LightGBM

Part 1: Gradient Boosting and LightGBM Fundamentals

In this part, we will initiate our exploration of machine learning by grounding you in its fundamental concepts, ranging from basic terminologies to intricate algorithms like random forests. We will delve deep into ensemble learning, highlighting the power of decision trees when combined, and then shift our focus to the gradient-boosting framework, LightGBM. Through hands-on examples in Python and comparative analyses against techniques like XGBoost and deep neural networks, you’ll gain both a foundational understanding and practical competence in the realm of machine learning, especially with LightGBM.

This part will include the following chapters:

  • Chapter 1, Introducing Machine Learning
  • Chapter 2, Ensemble Learning – Bagging and Boosting
  • Chapter 3, An Overview of LightGBM in Python
  • Chapter 4, Comparing LightGBM, XGBoost, and Deep Learning