Machine Learning Automation with TPOT: Build,validate,and deploy fully automated machine learning models with Python
by 作者: Dario Radečić
Publisher Finelybook 出版社: Packt Publishing (May 7,2021)
Language 语言: English
Pages 页数: 270 pages
ISBN-10 书号: 180056788X
ISBN-13 书号: 9781800567887
Book Description
Discover how TPOT can be used to handle automation in machine learning and explore the different types of tasks that TPOT can automate
Key Features
Understand parallelism and how to achieve it in Python.
Learn how to use neurons,layers,and activation functions and structure an artificial neural network.
Tune TPOT models to ensure optimum performance on previously unseen data.
The automation of machine learning tasks allows developers more time to focus on the usability and reactivity of the software powered by 作者: machine learning models. TPOT is a Python automated machine learning tool used for optimizing machine learning pipelines using genetic programming. Automating machine learning with TPOT enables individuals and companies to develop production-ready machine learning models cheaper and faster than with traditional methods.
With this practical guide to AutoML,developers working with Python on machine learning tasks will be able to put their knowledge to work and become productive quickly. You’ll adopt a hands-on approach to learning the implementation of AutoML and associated methodologies. Complete with step-by 作者: -step explanations of essential concepts,practical examples,and self-assessment questions,this book will show you how to build automated classification and regression models and compare their performance to custom-built models. As you advance,you’ll also develop state-of-the-art models using only a couple of lines of code and see how those models outperform all of your previous models on the same datasets.
by 作者: the end of this book,you’ll have gained the confidence to implement AutoML techniques in your organization on a production level.
What you will learn
Get to grips with building automated machine learning models
Build classification and regression models with impressive accuracy in a short time
Develop neural network classifiers with AutoML techniques
Compare AutoML models with traditional,manually developed models on the same datasets
Create robust,production-ready models
Evaluate automated classification models based on metrics such as accuracy,recall,precision,and f1-score
Get hands-on with deployment using Flask-RESTful on localhost
