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Mastering Java Machine Learning

Mastering Java Machine Learning

By : Kamath, Krishna Choppella
3.4 (9)
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Mastering Java Machine Learning

Mastering Java Machine Learning

3.4 (9)
By: Kamath, Krishna Choppella

Overview of this book

Java is one of the main languages used by practicing data scientists; much of the Hadoop ecosystem is Java-based, and it is certainly the language that most production systems in Data Science are written in. If you know Java, Mastering Machine Learning with Java is your next step on the path to becoming an advanced practitioner in Data Science. This book aims to introduce you to an array of advanced techniques in machine learning, including classification, clustering, anomaly detection, stream learning, active learning, semi-supervised learning, probabilistic graph modeling, text mining, deep learning, and big data batch and stream machine learning. Accompanying each chapter are illustrative examples and real-world case studies that show how to apply the newly learned techniques using sound methodologies and the best Java-based tools available today. On completing this book, you will have an understanding of the tools and techniques for building powerful machine learning models to solve data science problems in just about any domain.
Table of Contents (20 chapters)
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Mastering Java Machine Learning
Credits
Foreword
About the Authors
About the Reviewers
www.PacktPub.com
Customer Feedback
Preface
Linear Algebra
Index

References


  1. J. B. Lovins (1968). Development of a stemming algorithm, Mechanical Translation and Computer Linguistic, vol.11, no.1/2, pp. 22-31.

  2. Porter M.F, (1980). An algorithm for suffix stripping, Program; 14, 130-137.

  3. ZIPF, H.P., (1949). Human Behaviour and the Principle of Least Effort, Addison-Wesley, Cambridge, Massachusetts.

  4. LUHN, H.P., (1958). The automatic creation of literature abstracts', IBM Journal of Research and Development, 2, 159-165.

  5. Deerwester, S., Dumais, S., Furnas, G., & Landauer, T. (1990), Indexing by latent semantic analysis, Journal of the American Society for Information Sciences, 41, 391–407.

  6. Dempster, A. P., Laird, N. M., & Rubin, D. B. (1977), Maximum likelihood from incomplete data via the EM algorithm. Journal of the Royal Statistic Society, Series B, 39(1), 1–38.

  7. Greiff, W. R. (1998). A theory of term weighting based on exploratory data analysis. In 21st Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, New...

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