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Java: Data Science Made Easy

You're reading from   Java: Data Science Made Easy Data collection, processing, analysis, and more

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Product type Course
Published in Jul 2017
Publisher Packt
ISBN-13 9781788475655
Length 734 pages
Edition 1st Edition
Languages
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Authors (3):
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Alexey Grigorev Alexey Grigorev
Author Profile Icon Alexey Grigorev
Alexey Grigorev
Richard M. Reese Richard M. Reese
Author Profile Icon Richard M. Reese
Richard M. Reese
Jennifer L. Reese Jennifer L. Reese
Author Profile Icon Jennifer L. Reese
Jennifer L. Reese
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Toc

Table of Contents (29) Chapters Close

Title Page
Credits
Preface
1. Module 1 FREE CHAPTER
2. Getting Started with Data Science 3. Data Acquisition 4. Data Cleaning 5. Data Visualization 6. Statistical Data Analysis Techniques 7. Machine Learning 8. Neural Networks 9. Deep Learning 10. Text Analysis 11. Visual and Audio Analysis 12. Visual and Audio Analysis 13. Mathematical and Parallel Techniques for Data Analysis 14. Bringing It All Together 15. Module 2
16. Data Science Using Java 17. Data Processing Toolbox 18. Exploratory Data Analysis 19. Supervised Learning - Classification and Regression 20. Unsupervised Learning - Clustering and Dimensionality Reduction 21. Working with Text - Natural Language Processing and Information Retrieval 22. Extreme Gradient Boosting 23. Deep Learning with DeepLearning4J 24. Scaling Data Science 25. Deploying Data Science Models 26. Bibliography

Chapter 25. Deploying Data Science Models

So far we have covered a lot of data science models, we talked about many supervised and unsupervised learning methods, including deep learning and XGBoost, and discussed how we can apply these models to text and graph data.

In terms of the CRISP-DM methodology, we mostly covered the modeling part so far. But there are other important parts we have not yet discussed: evaluation and deployment. These steps are quite important in the application lifecycle, because the models we create should be useful for the business and bring value, and the only way to achieve that is integrate them into the application (the deployment part) and make sure they indeed are useful (the evaluation part).

In this last chapter of the book we will cover exactly these missing parts--we will see how we can deploy data science models so they can be used by other services of the application. In addition to that, we will also see how to perform an online evaluation of already...

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