This is the code repository for Machine Learning with LightGBM and Python, published by Packt.
A practitioner's guide to developing production-ready machine learning systems
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.
This book covers the following exciting features:
- Get an overview of ML and working with data and models in Python using scikit-learn
- Explore decision trees, ensemble learning, gradient boosting, DART, and GOSS
- Master LightGBM and apply it to classification and regression problems
- Tune and train your models using AutoML with FLAML and Optuna
- Build ML pipelines in Python to train and deploy models with secure and performant APIs
- Scale your solutions to production readiness with AWS Sagemaker, PostgresML, and Dask
If you feel this book is for you, get your copy today!
All of the code is organized into folders. For example, Chapter01.
The code will look like the following:
import numpy as np
import pandas as pd
from matplotlib import pyplot as plt
import seaborn as sns
from sklearn.linear_model import LinearRegression
from sklearn.metrics import mean_absolute_error
Following is what you need for this book: This book is for software engineers aspiring to be better machine learning engineers and data scientists unfamiliar with LightGBM, looking to gain in-depth knowledge of its libraries. Basic to intermediate Python programming knowledge is required to get started with the book. The book is also an excellent source for ML veterans, with a strong focus on ML engineering with up-to-date and thorough coverage of platforms such as AWS Sagemaker, PostgresML, and Dask.
With the following software and hardware list you can run all code files present in the book (Chapter 1-11).
| Chapter | Software required | OS required |
|---|---|---|
| 1-11 | Python 3.10 | Windows, macOS, or Linux |
| 1-11 | Anaconda 3 | Windows, macOS, or Linux |
| 1-11 | scikit-learn 1.2.1 | Windows, macOS, or Linux |
| 1-11 | LightGBM 3.3.5 | Windows, macOS, or Linux |
| 1-11 | XGBoost 1.7.4 | Windows, macOS, or Linux |
| 1-11 | Optuna 3.1.1 | Windows, macOS, or Linux |
| 1-11 | FLAML 1.2.3 | Windows, macOS, or Linux |
| 1-11 | FastAPI 0.103.1 | Windows, macOS, or Linux |
| 1-11 | Amazon SageMaker | Windows, macOS, or Linux |
| 1-11 | Docker 23.0.1 | Windows, macOS, or Linux |
| 1-11 | PostgresML 2.7.0 | Windows, macOS, or Linux |
| 1-11 | Dask 2023.7.1 | Windows, macOS, or Linux |
Andrich van Wyk has 15 years of experience in machine learning R&D, building AI-driven solutions, and consulting in the AI domain. He also has broad experience as a software engineer and architect with over a decade of industry experience working on enterprise systems. He graduated cum laude with an M.Sc. in Computer Science from the University of Pretoria, focusing on neural networks and evolutionary algorithms. Andrich enjoys writing about machine learning engineering and the software industry at large. He currently resides in South Africa with his wife and daughter.