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Mastering Java machine learning : mastering and implementing advanced techniques in machine learning /

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 beco...

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Detalles Bibliográficos
Clasificación:Libro Electrónico
Autores principales: Kamath, Uday (Autor), Choppella, Krishna (Autor)
Formato: Electrónico eBook
Idioma:Inglés
Publicado: Birmingham ; Mumbai : Packt, June 2017.
Temas:
Acceso en línea:Texto completo

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245 1 0 |a Mastering Java machine learning :  |b mastering and implementing advanced techniques in machine learning /  |c Dr. Uday Kamath, Krishna Choppella ; foreword by: Dr. Carlotta Domeniconi. 
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505 0 |a Mastering Java machine learning : mastering and implementing advanced techniques in machine learning -- Credits -- Foreword -- About the Authors -- About the Reviewers -- www.PacktPub.com -- Customer Feedback -- Table of Contents -- Preface -- 1. Machine Learning Review -- 2. Practical Approach to Real-World Supervised Learning -- 3. Unsupervised Machine Learning Techniques -- 4. Semi-Supervised and Active Learning -- 5. Real-Time Stream Machine Learning -- 6. Probabilistic Graph Modeling -- 7. Deep Learning -- 8. Text Mining and Natural Language Processing -- 9. Big Data Machine Learning -- The Final Frontier -- Appendix A: Linear Algebra -- Appendix B: Probability -- Index. 
520 3 |a 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. 
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