T�tulo: Hands-On Automated Machine Learning: A beginner's guide to building automated machine learning systems using AutoML and Python
Editor: Packt Publishing
Fecha de publicaci�n: 2018
Idioma: Ingl�s
ISBN 10: 1788629892
ISBN 13: 9781788629898
Encuadernaci�n: Encuadernaci�n de tapa blanda
Estado: New

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Sinopsis

Automate data and model pipelines for faster machine learning applications

Key Features

Build automated modules for different machine learning components
Understand each component of a machine learning pipeline in depth
Learn to use different open source AutoML and feature engineering platforms

Book Description

AutoML is designed to automate parts of Machine Learning. Readily available AutoML tools are making data science practitioners' work easy and are received well in the advanced analytics community. Automated Machine Learning covers the necessary foundation needed to create automated machine learning modules and helps you get up to speed with them in the most practical way possible.

In this book, you'll learn how to automate different tasks in the machine learning pipeline such as data preprocessing, feature selection, model training, model optimization, and much more. In addition to this, it demonstrates how you can use the available automation libraries, such as auto-sklearn and MLBox, and create and extend your own custom AutoML components for Machine Learning.

By the end of this book, you will have a clearer understanding of the different aspects of automated Machine Learning, and you'll be able to incorporate automation tasks using practical datasets. You can leverage your learning from this book to implement Machine Learning in your projects and get a step closer to winning various machine learning competitions.

What you will learn

Understand the fundamentals of Automated Machine Learning systems
Explore auto-sklearn and MLBox for AutoML tasks
Automate your preprocessing methods along with feature transformation
Enhance feature selection and generation using the Python stack
Assemble individual components of ML into a complete AutoML framework
Demystify hyperparameter tuning to optimize your ML models
Dive into Machine Learning concepts such as neural networks and autoencoders
Understand the information costs and trade-offs associated with AutoML

Who This Book Is For

If you're a budding data scientist, data analyst, or Machine Learning enthusiast and are new to the concept of automated machine learning, this book is ideal for you. You'll also find this book useful if you're an ML engineer or data professional interested in developing quick machine learning pipelines for your projects. Prior exposure to Python programming will help you get the best out of this book.

Introduction to AutoML
Introduction to Machine Learning Using Python
Data Preprocessing
Automated Algorithm Selection
Hyperparameter Optimization
Creating AutoML pipelines
Dive into Deep Learning
Critical Aspects of ML and Data Science Projects

Rese�a del editor

Automate data and model pipelines for faster machine learning applications Key Features Build automated modules for different machine learning components Understand each component of a machine learning pipeline in depth Learn to use different open source AutoML and feature engineering platforms Book DescriptionAutoML is designed to automate parts of Machine Learning. Readily available AutoML tools are making data science practitioners' work easy and are received well in the advanced analytics community. Automated Machine Learning covers the necessary foundation needed to create automated machine learning modules and helps you get up to speed with them in the most practical way possible. In this book, you'll learn how to automate different tasks in the machine learning pipeline such as data preprocessing, feature selection, model training, model optimization, and much more. In addition to this, it demonstrates how you can use the available automation libraries, such as auto-sklearn and MLBox, and create and extend your own custom AutoML components for Machine Learning. By the end of this book, you will have a clearer understanding of the different aspects of automated Machine Learning, and you'll be able to incorporate automation tasks using practical datasets. You can leverage your learning from this book to implement Machine Learning in your projects and get a step closer to winning various machine learning competitions. What you will learn Understand the fundamentals of Automated Machine Learning systems Explore auto-sklearn and MLBox for AutoML tasks Automate your preprocessing methods along with feature transformation Enhance feature selection and generation using the Python stack Assemble individual components of ML into a complete AutoML framework Demystify hyperparameter tuning to optimize your ML models Dive into Machine Learning concepts such as neural networks and autoencoders Understand the information costs and trade-offs associated with AutoML Who this book is forIf you're a budding data scientist, data analyst, or Machine Learning enthusiast and are new to the concept of automated machine learning, this book is ideal for you. You'll also find this book useful if you're an ML engineer or data professional interested in developing quick machine learning pipelines for your projects. Prior exposure to Python programming will help you get the best out of this book.

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