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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 (13 chapters)
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10
A. Linear Algebra
12
Index

Issues specific to unsupervised learning


The following are some issues that pertain to unsupervised learning techniques:

  • Parameter setting: Deciding on number of features, usefulness of features, number of clusters, shapes of clusters, and so on, pose enormous challenges to certain unsupervised methods

  • Evaluation methods: Since unsupervised learning methods are ill-posed due to lack of ground-truth, evaluation of algorithms becomes very subjective.

  • Hard or soft labeling: Many unsupervised learning problems require giving labels to the data in an exclusive or probabilistic manner. This poses a problem for many algorithms

  • Interpretability of results and models: Unlike supervised learning, the lack of ground truth and the nature of some algorithms make interpreting the results from both model and labeling even more difficult

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