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Alles is data in onze gedigitaliseerde maatschappij. Individuen, organisaties en apparaten slaan steeds meer informatie op, zowel op gestructureerde als ongestructureerde wijze. Het resultaat? Steeds grotere datasets die met traditionele software nauwelijks nog te analyseren zijn, de zogeheten Big Data.Big Data is meer dan een hype, het is de toekomst. Veel organisaties zijn zich wel bewust van de waarde van grote datasets en beseffen dat ze ?iets? met die schat aan informatie moeten doen. Maar om daadwerkelijk data driven te worden, moet je data leren begrijpen en de juiste vragen aan de beschikbare gegevens stellen.In dit boek worden de Big Data-wereld, de aanpak, de mogelijkheden en de toepassingen stap voor stap uitgelegd, gericht op de praktijk en het gebruik. De auteurs beschrijven in heldere bewoordingen de basis, maar gaan ook dieper in op gevorderde onderwerpen. Met dit boek kan iedereen aan de slag en tot verrassende resultaten komen.Bron: bol.com

computer --- Big Data --- Maatschappij --- Praktijkonderzoek --- Certificaat --- Datawetenschap --- Dataset --- Big data --- Databeheer

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This eBook is a collection of articles from a Frontiers Research Topic. Frontiers Research Topics are very popular trademarks of the Frontiers Journals Series: they are collections of at least ten articles, all centered on a particular subject. With their unique mix of varied contributions from Original Research to Review Articles, Frontiers Research Topics unify the most influential researchers, the latest key findings and historical advances in a hot research area! Find out more on how to host your own Frontiers Research Topic or contribute to one as an author by contacting the Frontiers Editorial Office: frontiersin.org/about/contact

Science: general issues --- Botany & plant sciences --- multi-locus genome-wide association study --- mixed linear model --- mrMLM --- FASTmrMLM --- FASTmrEMMA --- ISIS EM-BLASSO --- pLARmEB --- pKWmEB --- complex traits --- omics big dataset

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This book contains a fast-paced introduction to data-related tasks in preparation for training models ondatasets. It presents a step-by-step, Python-based code sample that uses the kNN algorithm to manage a model on a dataset.Chapter One begins with an introduction to datasets and issues that can arise, followed by Chapter Two on outliers and anomaly detection. The next chapter explores ways for handling missing data and invalid data, and Chapter Four demonstrates how to train models with classification algorithms. Chapter 5 introduces visualization toolkits, such as Sweetviz, Skimpy, Matplotlib, and Seaborn, along with some simple Python-based code samples that render charts and graphs. An appendix includes some basics on using awk. Companion files with code, datasets, and figures are available for downloading.FEATURES:Covers extensive topics related to cleaning datasets and working with modelsIncludes Python-based code samples and a separate chapter on Matplotlib and SeabornFeatures companion files with source code, datasets, and figures from the book

Data Mining --- Computers --- Python (Computer program language). --- Python (Computer program language) --- Matplotlib. --- Python-based code. --- Seaborn. --- Skimpy. --- Sweetviz. --- anomaly detection. --- data analysis. --- dataset. --- kNN algorithm. --- model. --- visualization.

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This book is to chart the progress in applying machine learning, including deep learning, to a broad range of image analysis and pattern recognition problems and applications. In this book, we have assembled original research articles making unique contributions to the theory, methodology and applications of machine learning in image analysis and pattern recognition.

Information technology industries --- machine learning --- deep learning --- image processing --- classification --- tea --- fermentation --- automated image coding --- data collection methods --- interdisciplinary learning theory --- research methods --- systematic literature review --- visitor use management --- image classification --- multi-instance learning --- divergence --- dissimilarity --- bag-to-class --- Kullback–Leibler --- segment-based temporal modeling --- two-stream network --- action recognition --- internet of things --- detection --- dataset --- plant disease recognition --- image segmentation --- aphid --- Aphoidea --- lemon --- breast cancer mammogram dataset --- ultrasound breast cancer scans --- BI-RADS --- clinical data

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This book is to chart the progress in applying machine learning, including deep learning, to a broad range of image analysis and pattern recognition problems and applications. In this book, we have assembled original research articles making unique contributions to the theory, methodology and applications of machine learning in image analysis and pattern recognition.

machine learning --- deep learning --- image processing --- classification --- tea --- fermentation --- automated image coding --- data collection methods --- interdisciplinary learning theory --- research methods --- systematic literature review --- visitor use management --- image classification --- multi-instance learning --- divergence --- dissimilarity --- bag-to-class --- Kullback–Leibler --- segment-based temporal modeling --- two-stream network --- action recognition --- internet of things --- detection --- dataset --- plant disease recognition --- image segmentation --- aphid --- Aphoidea --- lemon --- breast cancer mammogram dataset --- ultrasound breast cancer scans --- BI-RADS --- clinical data