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Feature Engineering For Machine Learning And Data Analytics

Feature Engineering for Machine Learning and Data Analytics PDF
Author: Guozhu Dong
Publisher: CRC Press
ISBN: 1351721267
Size: 33.59 MB
Format: PDF, Mobi
Category : Business & Economics
Languages : en
Pages : 400
View: 1049

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Feature Engineering For Machine Learning And Data Analytics

by Guozhu Dong, Feature Engineering For Machine Learning And Data Analytics Books available in PDF, EPUB, Mobi Format. Download Feature Engineering For Machine Learning And Data Analytics books, Feature engineering plays a vital role in big data analytics. Machine learning and data mining algorithms cannot work without data. Little can be achieved if there are few features to represent the underlying data objects, and the quality of results of those algorithms largely depends on the quality of the available features. Feature Engineering for Machine Learning and Data Analytics provides a comprehensive introduction to feature engineering, including feature generation, feature extraction, feature transformation, feature selection, and feature analysis and evaluation. The book presents key concepts, methods, examples, and applications, as well as chapters on feature engineering for major data types such as texts, images, sequences, time series, graphs, streaming data, software engineering data, Twitter data, and social media data. It also contains generic feature generation approaches, as well as methods for generating tried-and-tested, hand-crafted, domain-specific features. The first chapter defines the concepts of features and feature engineering, offers an overview of the book, and provides pointers to topics not covered in this book. The next six chapters are devoted to feature engineering, including feature generation for specific data types. The subsequent four chapters cover generic approaches for feature engineering, namely feature selection, feature transformation based feature engineering, deep learning based feature engineering, and pattern based feature generation and engineering. The last three chapters discuss feature engineering for social bot detection, software management, and Twitter-based applications respectively. This book can be used as a reference for data analysts, big data scientists, data preprocessing workers, project managers, project developers, prediction modelers, professors, researchers, graduate students, and upper level undergraduate students. It can also be used as the primary text for courses on feature engineering, or as a supplement for courses on machine learning, data mining, and big data analytics.




Feature Engineering For Machine Learning

Feature Engineering for Machine Learning PDF
Author: Alice Zheng
Publisher: "O'Reilly Media, Inc."
ISBN: 1491953195
Size: 37.81 MB
Format: PDF, ePub, Docs
Category : Computers
Languages : en
Pages : 218
View: 7430

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Feature Engineering For Machine Learning

by Alice Zheng, Feature Engineering For Machine Learning Books available in PDF, EPUB, Mobi Format. Download Feature Engineering For Machine Learning books, Feature engineering is a crucial step in the machine-learning pipeline, yet this topic is rarely examined on its own. With this practical book, you’ll learn techniques for extracting and transforming features—the numeric representations of raw data—into formats for machine-learning models. Each chapter guides you through a single data problem, such as how to represent text or image data. Together, these examples illustrate the main principles of feature engineering. Rather than simply teach these principles, authors Alice Zheng and Amanda Casari focus on practical application with exercises throughout the book. The closing chapter brings everything together by tackling a real-world, structured dataset with several feature-engineering techniques. Python packages including numpy, Pandas, Scikit-learn, and Matplotlib are used in code examples. You’ll examine: Feature engineering for numeric data: filtering, binning, scaling, log transforms, and power transforms Natural text techniques: bag-of-words, n-grams, and phrase detection Frequency-based filtering and feature scaling for eliminating uninformative features Encoding techniques of categorical variables, including feature hashing and bin-counting Model-based feature engineering with principal component analysis The concept of model stacking, using k-means as a featurization technique Image feature extraction with manual and deep-learning techniques




The Art Of Feature Engineering

The Art of Feature Engineering PDF
Author: Pablo Duboue
Publisher: Cambridge University Press
ISBN: 1108709389
Size: 35.17 MB
Format: PDF
Category : Computers
Languages : en
Pages : 283
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The Art Of Feature Engineering

by Pablo Duboue, The Art Of Feature Engineering Books available in PDF, EPUB, Mobi Format. Download The Art Of Feature Engineering books, A practical guide for data scientists who want to improve the performance of any machine learning solution with feature engineering.




Datenintensive Anwendungen Designen

Datenintensive Anwendungen designen PDF
Author: Martin Kleppmann
Publisher: O'Reilly
ISBN: 3960101848
Size: 21.19 MB
Format: PDF, ePub
Category : Computers
Languages : de
Pages : 652
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Datenintensive Anwendungen Designen

by Martin Kleppmann, Datenintensive Anwendungen Designen Books available in PDF, EPUB, Mobi Format. Download Datenintensive Anwendungen Designen books, Daten stehen heute im Mittelpunkt vieler Herausforderungen im Systemdesign. Dabei sind komplexe Fragen wie Skalierbarkeit, Konsistenz, Zuverlässigkeit, Effizienz und Wartbarkeit zu klären. Darüber hinaus verfügen wir über eine überwältigende Vielfalt an Tools, einschließlich relationaler Datenbanken, NoSQL-Datenspeicher, Stream-und Batchprocessing und Message Broker. Aber was verbirgt sich hinter diesen Schlagworten? Und was ist die richtige Wahl für Ihre Anwendung? In diesem praktischen und umfassenden Leitfaden unterstützt Sie der Autor Martin Kleppmann bei der Navigation durch dieses schwierige Terrain, indem er die Vor-und Nachteile verschiedener Technologien zur Verarbeitung und Speicherung von Daten aufzeigt. Software verändert sich ständig, die Grundprinzipien bleiben aber gleich. Mit diesem Buch lernen Softwareentwickler und -architekten, wie sie die Konzepte in der Praxis umsetzen und wie sie Daten in modernen Anwendungen optimal nutzen können. Inspizieren Sie die Systeme, die Sie bereits verwenden, und erfahren Sie, wie Sie sie effektiver nutzen können Treffen Sie fundierte Entscheidungen, indem Sie die Stärken und Schwächen verschiedener Tools kennenlernen Steuern Sie die notwenigen Kompromisse in Bezug auf Konsistenz, Skalierbarkeit, Fehlertoleranz und Komplexität Machen Sie sich vertraut mit dem Stand der Forschung zu verteilten Systemen, auf denen moderne Datenbanken aufbauen Werfen Sie einen Blick hinter die Kulissen der wichtigsten Onlinedienste und lernen Sie von deren Architekturen




Python Feature Engineering Cookbook

Python Feature Engineering Cookbook PDF
Author: Soledad Galli
Publisher:
ISBN: 9781789806311
Size: 55.32 MB
Format: PDF, ePub
Category : Computers
Languages : en
Pages : 372
View: 1772

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Python Feature Engineering Cookbook

by Soledad Galli, Python Feature Engineering Cookbook Books available in PDF, EPUB, Mobi Format. Download Python Feature Engineering Cookbook books, Extract accurate information from data to train and improve machine learning models using NumPy, SciPy, pandas, and scikit-learn libraries Key Features Discover solutions for feature generation, feature extraction, and feature selection Uncover the end-to-end feature engineering process across continuous, discrete, and unstructured datasets Implement modern feature extraction techniques using Python's pandas, scikit-learn, SciPy and NumPy libraries Book Description Feature engineering is invaluable for developing and enriching your machine learning models. In this cookbook, you will work with the best tools to streamline your feature engineering pipelines and techniques and simplify and improve the quality of your code. Using Python libraries such as pandas, scikit-learn, Featuretools, and Feature-engine, you'll learn how to work with both continuous and discrete datasets and be able to transform features from unstructured datasets. You will develop the skills necessary to select the best features as well as the most suitable extraction techniques. This book will cover Python recipes that will help you automate feature engineering to simplify complex processes. You'll also get to grips with different feature engineering strategies, such as the box-cox transform, power transform, and log transform across machine learning, reinforcement learning, and natural language processing (NLP) domains. By the end of this book, you'll have discovered tips and practical solutions to all of your feature engineering problems. What you will learn Simplify your feature engineering pipelines with powerful Python packages Get to grips with imputing missing values Encode categorical variables with a wide set of techniques Extract insights from text quickly and effortlessly Develop features from transactional data and time series data Derive new features by combining existing variables Understand how to transform, discretize, and scale your variables Create informative variables from date and time Who this book is for This book is for machine learning professionals, AI engineers, data scientists, and NLP and reinforcement learning engineers who want to optimize and enrich their machine learning models with the best features. Knowledge of machine learning and Python coding will assist you with understanding the concepts covered in this book.




Feature Engineering Made Easy

Feature Engineering Made Easy PDF
Author: Sinan Ozdemir
Publisher: Packt Publishing Ltd
ISBN: 1787286479
Size: 35.52 MB
Format: PDF, ePub, Mobi
Category : Computers
Languages : en
Pages : 316
View: 5100

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Feature Engineering Made Easy

by Sinan Ozdemir, Feature Engineering Made Easy Books available in PDF, EPUB, Mobi Format. Download Feature Engineering Made Easy books, 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.




Einf Hrung In Machine Learning Mit Python

Einf  hrung in Machine Learning mit Python PDF
Author: Andreas C. Müller
Publisher: O'Reilly
ISBN: 3960101112
Size: 10.64 MB
Format: PDF, Mobi
Category : Computers
Languages : de
Pages : 378
View: 6958

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Einf Hrung In Machine Learning Mit Python

by Andreas C. Müller, Einf Hrung In Machine Learning Mit Python Books available in PDF, EPUB, Mobi Format. Download Einf Hrung In Machine Learning Mit Python books, Machine Learning ist zu einem wichtigen Bestandteil vieler kommerzieller Anwendungen und Forschungsprojekte geworden, von der medizinischen Diagnostik bis hin zur Suche nach Freunden in sozialen Netzwerken. Um Machine-Learning-Anwendungen zu entwickeln, braucht es keine großen Expertenteams: Wenn Sie Python-Grundkenntnisse mitbringen, zeigt Ihnen dieses Praxisbuch, wie Sie Ihre eigenen Machine-Learning-Lösungen erstellen. Mit Python und der scikit-learn-Bibliothek erarbeiten Sie sich alle Schritte, die für eine erfolgreiche Machine-Learning-Anwendung notwendig sind. Die Autoren Andreas Müller und Sarah Guido konzentrieren sich bei der Verwendung von Machine-Learning-Algorithmen auf die praktischen Aspekte statt auf die Mathematik dahinter. Wenn Sie zusätzlich mit den Bibliotheken NumPy und matplotlib vertraut sind, hilft Ihnen dies, noch mehr aus diesem Tutorial herauszuholen. Das Buch zeigt Ihnen: - grundlegende Konzepte und Anwendungen von Machine Learning - Vor- und Nachteile weit verbreiteter maschineller Lernalgorithmen - wie sich die von Machine Learning verarbeiteten Daten repräsentieren lassen und auf welche Aspekte der Daten Sie sich konzentrieren sollten - fortgeschrittene Methoden zur Auswertung von Modellen und zum Optimieren von Parametern - das Konzept von Pipelines, mit denen Modelle verkettet und Arbeitsabläufe gekapselt werden - Arbeitsmethoden für Textdaten, insbesondere textspezifische Verarbeitungstechniken - Möglichkeiten zur Verbesserung Ihrer Fähigkeiten in den Bereichen Machine Learning und Data Science Dieses Buch ist eine fantastische, super praktische Informationsquelle für jeden, der mit Machine Learning in Python starten möchte – ich wünschte nur, es hätte schon existiert, als ich mit scikit-learn anfing! Hanna Wallach, Senior Researcher, Microsoft Research




Feature Engineering Made Easy

Feature Engineering Made Easy PDF
Author: Sinan Ozdemir
Publisher:
ISBN: 9781787287600
Size: 13.41 MB
Format: PDF, Mobi
Category : Computers
Languages : en
Pages : 316
View: 5474

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Feature Engineering Made Easy

by Sinan Ozdemir, Feature Engineering Made Easy Books available in PDF, EPUB, Mobi Format. Download Feature Engineering Made Easy books, 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.




Machine Learning Mit Python Das Praxis Handbuch Fur Data Science Predictive Analytics Und Deep Learning

MACHINE LEARNING MIT PYTHON DAS PRAXIS HANDBUCH FUR DATA SCIENCE  PREDICTIVE ANALYTICS UND DEEP LEARNING  PDF
Author: SEBASTIAN RASCHKA.
Publisher:
ISBN: 9783958454231
Size: 11.84 MB
Format: PDF, Kindle
Category :
Languages : de
Pages :
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Machine Learning Mit Python Das Praxis Handbuch Fur Data Science Predictive Analytics Und Deep Learning

by SEBASTIAN RASCHKA., Machine Learning Mit Python Das Praxis Handbuch Fur Data Science Predictive Analytics Und Deep Learning Books available in PDF, EPUB, Mobi Format. Download Machine Learning Mit Python Das Praxis Handbuch Fur Data Science Predictive Analytics Und Deep Learning books,