Machine Learning with LightGBM and Python: A practitioner's guide to developing production-ready machine learning systems (Paperback)

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

Key Features:

• Get started with LightGBM, a powerful gradient-boosting library for building ML solutions
• Apply data science processes to real-world problems through case studies
• Elevate your software by building machine learning solutions on scalable platforms
• Purchase of the print or Kindle book includes a free PDF eBook

Book Description:

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 state-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.

What You Will Learn:

• 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

Who this book is for:

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.

將您提供的文字翻譯成繁體中文如下：

將您的軟體提升到更高的水平，並通過使用LightGBM和Python來解決真實世界的數據科學問題，建立可投入生產的機器學習解決方案。

主要特點：
- 開始使用LightGBM，一個強大的梯度提升庫，用於構建機器學習解決方案
- 通過案例研究將數據科學流程應用於真實世界問題
- 通過在可擴展平台上構建機器學習解決方案提升您的軟體
- 購買印刷版或Kindle電子書將包含免費的PDF電子書

書籍描述：
《使用LightGBM和Python進行機器學習》是一本全面指南，從學習機器學習的基礎知識開始，進而構建可擴展的機器學習系統，以供發布使用。

本書將使您熟悉高性能梯度提升框架LightGBM，並向您展示如何使用它來解決各種機器學習問題，以產生高度準確、強健且具有預測性的解決方案。

從scikit-learn中的簡單機器學習模型開始，您將探索梯度提升機和LightGBM的細節。通過各種案例研究的指導，更好地理解數據科學流程，並學習如何將您的技能實際應用於真實世界問題。

隨著您的進展，您將通過學習如何使用Python工具（如FastAPI）構建和集成可擴展的機器學習流程來提升您的軟體工程技能，以處理數據、訓練模型並部署它們以提供安全的API。

通過閱讀本書，您將熟練使用各種最先進的工具，這些工具將幫助您構建可投入生產的系統，包括FLAML用於自動機器學習、PostgresML用於使用Postgres操作機器學習流程、使用Dask進行高性能分佈式訓練和服務，以及在AWS Sagemaker中創建和運行模型。

學到的內容：
- 瞭解機器學習、使用scikit-learn在Python中處理數據和模型的概述
- 探索決策樹、集成學習、梯度提升、DART和GOSS
- 掌握LightGBM並將其應用於分類和回歸問題
- 使用FLAML和Optuna進行自動機器學習來調整和訓練模型
- 在Python中構建機器學習流程，以訓練和部署具有安全和高性能API的模型
- 使用AWS Sagemaker、PostgresML和Dask將解決方案擴展到生產就緒狀態

適合閱讀對象：
本書適合希望成為更好的機器學習工程師和對LightGBM不熟悉的數據科學家的軟體工程師，他們希望獲得深入了解其庫的知識。開始閱讀本書需要基本到中級的Python編程知識。

本書也是ML老手的優秀資源，重點關注ML工程，並全面介紹了AWS Sagemaker、PostgresML和Dask等平台。