Title: Machine Learning with LightGBM and Python: A practitioner's guide to developing production-ready machine learning systems
Publisher: Packt Publishing
Publication Date: 2023
Language: English
ISBN 10: 1800564740
ISBN 13: 9781800564749
Binding: Paperback
Condition: Very Good
Weight: 558 grams

About this title

Synopsis

Take your software to the next level and solve real-world data science problems by learning to build production-ready machine-learning solutions using LightGBM and Python.

Key Features

Start your ML journey with LightGBM, a powerful gradient-boosting library for building ML solutions
Learn to apply the data science process to real-world problems through case studies
Elevate your software by learning to build production-ready machine-learning solutions on scalable platforms

Book Description

Machine Learning with LightGBM and Python is a comprehensive guide for learning the basics of machine learning and progressing to building scalable, production-ready machine learning systems.

At the core of the book is the LightGBM library. LightGBM is a high-performance gradient-boosting framework that can be used on various machine-learning problems to produce highly accurate, robust predictive solutions.

Starting with simple machine learning models in scikit-learn, you will learn about the intricacies of gradient boosting machines and LightGBM. You will be guided through various case studies to better understand the data science process and learn how to practically apply your skills to real-world problems.

Elevate your software engineering skills by learning to build and integrate scalable machine-learning pipelines to process data, train models and deploy them for serving behind secure APIs using Python tools such as FastAPI.

Various -of-the-art tools will also be covered to enable you to 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.

What you will learn

An overview of machine learning and working with data and models in Python using scikit-learn
Learn about decision trees, ensemble learning, gradient boosting, and advanced topics such as DART and GOSS
Master LightGBM and learn to apply it to classification and regression problems
Tune and train your models using AutoML with FLAML and Optuna
Build machine-learning pipelines in Python to train and deploy models behind secure, performant APIs
Scale your solutions to production readiness with platforms such as AWS Sagemaker, PostgresML and Dask

Who This Book Is For

This book is intended for software engineers aspiring to be better machine learning engineers. Further, data scientists unfamiliar with LightGBM will gain in-depth knowledge about the library and its application.

Basic to intermediate Python programming knowledge is required to get started with the book. Later chapters will incorporate more advanced programming but should remain accessible to all readers.

The book is also excellent for ML veterans, with a strong focus on ML engineering through up-to-date and thorough coverage of platforms such as AWS Sagemaker, PostgresML and Dask.

An Introduction Machine Learning and Decision Trees
Decision Tree Ensembles: Bagging and Boosting
An Overview of LightGBM in Python
LightGBM, XGBoost and Deep Learning
LightGBM Parameter Optimization and Tuning with Optuna
Solving Real World Problems with LightGBM
LightGBM AutoML with FLAML
Machine Learning Pipelines with LightGBM
Deploying LightGBM to AWS SageMaker
Deploying LightGBM with PostgresML
Distributed Training and Serving of LightGBM using Dask

About the Author

Andrich van Wyk has 15 years of experience in machine learning R&D and building AI-driven solutions. 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. His work focused on neural networks and population-based algorithms such as Particle Swarm Optimization and Honey-Bee Foraging.

Andrich also writes about software and machine learning on his blog and his Substack. He currently resides in South Africa with his wife and daughter.

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