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Machine Learning with LightGBM and Python. A practitioner's guide to developing production-ready machine learning systems Andrich van Wyk

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Machine Learning with LightGBM and Python. A practitioner's guide to developing production-ready machine learning systems Andrich van Wyk - okladka książki

Machine Learning with LightGBM and Python. A practitioner's guide to developing production-ready machine learning systems Andrich van Wyk - okladka książki

Autor:
Andrich van Wyk
Wydawnictwo:
Packt Publishing (Z chęcią przeczytam książkę w języku polskim)
Ocena:
Bądź pierwszym, który oceni tę książkę
Stron:
252
Dostępne formaty:
     PDF
     ePub

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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.

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O autorze książki

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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