Machine Learning with LightGBM and Python
eBook Details:
- Paperback: 252 pages
- Publisher: WOW! eBook (September 29, 2023)
- Language: English
- ISBN-10: 1800564740
- ISBN-13: 978-1800564749
eBook Description:
Machine Learning with LightGBM and Python: 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
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.
- 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
By the end of this Machine Learning with LightGBM and Python 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.