Feature Engineering Made Easy: Identify unique features from your dataset in order to build powerful machine learning systems
Idioma: inglés
Editorial: Packt Publishing Limited, United Kingdom, Birmingham, 2023
- Tapa blanda
- Usado
Librería: WorldofBooks, Goring-By-Sea, WS, Reino UnidoWorldofBooks
Vendedor de AbeBooks desde el 16 de marzo de 2007
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EUR 30,38
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A perfect guide to speed up the predicting power of machine learning algorithms About This Book Design, discover, and create dynamic, efficient features for your machine learning application Understand your data in-depth and derive astonishing data insights with the help of this Guide Grasp powerful feature-engineering techniques and build machine learning systems Who This Book Is For If you are a data science professional or a machine learning engineer looking to strengthen your predictive analytics model, then this book is a perfect guide for you. Some basic understanding of the machine learning concepts and Python scripting would be enough to get started with this book. What You Will Learn Identify and leverage different feature types Clean features in data to improve predictive power Understand why and how to perform feature selection, and model error analysis Leverage domain knowledge to construct new features Deliver features based on mathematical insights Use machine-learning algorithms to construct features Master feature engineering and optimization Harness feature engineering for real world applications through a structured case study In Detail Feature engineering is the most important step in creating powerful machine learning systems. This book will take you through the entire feature-engineering journey to make your machine learning much more systematic and effective. You will start with understanding your dataoften the success of your ML models depends on how you leverage different feature types, such as continuous, categorical, and more, You will learn when to include a feature, when to omit it, and why, all by understanding error analysis and the acceptability of your models. You will learn to convert a problem statement into useful new features. You will learn to deliver features driven by business needs as well as mathematical insights. You'll also learn how to use machine learning on your machines, automatically learning amazing features for your data. By the end of the book, you will become proficient in Feature Selection, Feature Learning, and Feature Optimization. Style and approach This step-by-step guide with use cases, examples, and illustrations will help you master the concepts of feature engineering. Along with explaining the fundamentals, the book will also introduce you to slightly advanced concepts later on and will help you implement these techniques in the real world. The book has been read, but is in excellent condition. Pages are intact and not marred by notes or highlighting. The spine remains undamaged.…
N° de ref. del artículo GOR009975604
- Título
- Feature Engineering Made Easy: Identify unique features from your dataset in order to build powerful machine learning systems
- Autor
- Sinan Ozdemir
- Editorial
- Packt Publishing Limited, United Kingdom, Birmingham
- Año de publicación
- 2023
- Estado
- Very Good
- Encuadernación
- Paperback
- Idioma
- inglés
- ISBN 10
- 1787287602
- ISBN 13
- 9781787287600
- Peso del artículo
- 649 gramos
- Dimensiones
- 189.99 x 14.99
A perfect guide to speed up the predicting power of machine learning algorithms
Key Features:
- Design, discover, and create dynamic, efficient features for your machine learning application
- Understand your data in-depth and derive astonishing data insights with the help of this Guide
- Grasp powerful feature-engineering techniques and build machine learning systems
Book Description:
Feature engineering is the most important step in creating powerful machine learning systems. This book will take you through the entire feature-engineering journey to make your machine learning much more systematic and effective.
You will start with understanding your data-often the success of your ML models depends on how you leverage different feature types, such as continuous, categorical, and more, You will learn when to include a feature, when to omit it, and why, all by understanding error analysis and the acceptability of your models. You will learn to convert a problem statement into useful new features. You will learn to deliver features driven by business needs as well as mathematical insights. You'll also learn how to use machine learning on your machines, automatically learning amazing features for your data.
By the end of the book, you will become proficient in Feature Selection, Feature Learning, and Feature Optimization.
What You Will Learn:
Identify and leverage different feature types
Clean features in data to improve predictive power
Understand why and how to perform feature selection, and model error analysis
Leverage domain knowledge to construct new features
Deliver features based on mathematical insights
Use machine-learning algorithms to construct features
Master feature engineering and optimization
Harness feature engineering for real world applications through a structured case study
Who this book is for:
If you are a data science professional or a machine learning engineer looking to strengthen your predictive analytics model, then this book is a perfect guide for you. Some basic understanding of the machine learning concepts and Python scripting would be enough to get started with this book.
“Sinopsis” puede pertenecer a otra edición de este título.
Acerca del autor
Divya Susarla is an experienced leader in data methods, implementing and applying tactics across a range of industries and fields including investment management, social enterprise consulting, and wine marketing. She trained in data by way of specializing in Economics and Political Science at University of California, Irvine, cultivating a passion for teaching by developing an analytically based, international affairs curriculum for students through the Global Connect program. Divya is currently focused on natural language processing and generation techniques at Kylie.ai, a startup helping clients automate their customer support conversations. When she is not busy working on building Kylie.ai and writing educational content, she spends her time traveling across the globe and experimenting with new recipes at her home in Berkeley, CA.
“Acerca de” puede pertenecer a otra edición de este título.
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