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:
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Understand parallelism and how to achieve it in Python.
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Learn how to use neurons, layers, and activation functions and
structure an artificial neural network.
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Tune TPOT models to ensure optimum performance on previously unseen
data.
Book Description:
The automation of machine learning tasks allows developers more time to
focus on the usability and reactivity of the software powered by 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-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 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:
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Get to grips with building automated machine learning models
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Build classification and regression models with impressive accuracy in
a short time
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Develop neural network classifiers with AutoML techniques
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Compare AutoML models with traditional, manually developed models on
the same datasets
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Create robust, production-ready models
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Evaluate automated classification models based on metrics such as
accuracy, recall, precision, and f1-score
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Get hands-on with deployment using Flask-RESTful on localhost
Who this book is for:
Data scientists, data analysts, and software developers who are new to
machine learning and want to use it in their applications will find this
book useful. This book is also for business users looking to automate
business tasks with machine learning. Working knowledge of the Python
programming language and beginner-level understanding of machine
learning are necessary to get started.
