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Learning Data Mining with Python

You're reading from   Learning Data Mining with Python Use Python to manipulate data and build predictive models

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Product type Paperback
Published in Apr 2017
Publisher Packt
ISBN-13 9781787126787
Length 358 pages
Edition 2nd Edition
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Concepts
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Table of Contents (14) Chapters Close

Preface 1. Getting Started with Data Mining FREE CHAPTER 2. Classifying with scikit-learn Estimators 3. Predicting Sports Winners with Decision Trees 4. Recommending Movies Using Affinity Analysis 5. Features and scikit-learn Transformers 6. Social Media Insight using Naive Bayes 7. Follow Recommendations Using Graph Mining 8. Beating CAPTCHAs with Neural Networks 9. Authorship Attribution 10. Clustering News Articles 11. Object Detection in Images using Deep Neural Networks 12. Working with Big Data 13. Next Steps...

Classifying Objects in Images Using Deep Learning


The following topics are also important when deeper study into Classifying objects is considered.

Mahotas

URL: http://luispedro.org/software/mahotas/

Another package for image processing is Mahotas, including better and more complex image processing techniques that can help achieve better accuracy, although they may come at a high computational cost. However, many image processing tasks are good candidates for parallelization. More techniques on image classification can be found in the research literature, with this survey paper as a good start: http://ijarcce.com/upload/january/22-A%20Survey%20on%20Image%20Classification.pdf.

Other image datasets are available at http://rodrigob.github.io/are_we_there_yet/build/classification_datasets_results.html.

There are many datasets of images available from a number of academic and industry-based sources. The linked website lists a bunch of datasets and some of the best algorithms to use on them. Implementing...

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