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Machine Learning for Data Science Handbook

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Cover of 'Machine Learning for Data Science Handbook'

Table of Contents

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    Book Overview
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    Chapter 1 Data Science and Knowledge Discovery Using Machine Learning Methods
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    Chapter 2 Handling Missing Attribute Values
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    Chapter 3 Data Integration Process Automation Using Machine Learning: Issues and Solution
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    Chapter 4 Rule Induction
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    Chapter 5 Nearest-Neighbor Methods: A Modern Perspective
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    Chapter 6 Support Vector Machines
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    Chapter 7 Empowering Interpretable, Explainable Machine Learning Using Bayesian Network Classifiers
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    Chapter 8 Soft Decision Trees
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    Chapter 9 Quality Assessment and Evaluation Criteria in Supervised Learning
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    Chapter 10 Trajectory Clustering Analysis
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    Chapter 11 Clustering High-Dimensional Data
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    Chapter 12 Fuzzy C-Means Clustering: Advances and Challenges (Part II)
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    Chapter 13 Clustering in Streams
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    Chapter 14 Introduction to Deep Learning
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    Chapter 15 Graph Embedding
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    Chapter 16 Autoencoders
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    Chapter 17 Generative Adversarial Networks
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    Chapter 18 Spatial Data Science
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    Chapter 19 Multimedia Data Learning
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    Chapter 20 Web Mining
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    Chapter 21 Mining Temporal Data
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    Chapter 22 Cloud Big Data Mining and Analytics: Bringing Greenness and Acceleration in the Cloud
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    Chapter 23 Multi-Label Ranking: Mining Multi-Label and Label Ranking Data
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    Chapter 24 Reinforcement Learning for Data Science
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    Chapter 25 Adversarial Machine Learning
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    Chapter 26 Ensembled Transferred Embeddings
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    Chapter 27 Data Mining in Medicine
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    Chapter 28 Recommender Systems
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    Chapter 29 Activity Recognition
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    Chapter 30 Social Network Analysis for Disinformation Detection
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    Chapter 31 Online Propaganda Detection
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    Chapter 32 Interpretable Machine Learning forFinancial Applications
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    Chapter 33 Predictive Analytics for Targeting Decisions
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    Chapter 34 Machine Learning for the Geosciences
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    Chapter 35 Sentiment Analysis for Social Text
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    Chapter 36 Human Resources-Based Organizational Data Mining (HRODM): Themes, Trends, Focus, Future
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    Chapter 37 Algorithmic Fairness
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    Chapter 38 Privacy-Preserving Data Mining (PPDM)
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    Chapter 39 Explainable Machine Learning and Visual Knowledge Discovery
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    Chapter 40 Visual Analytics and Human Involvement in Machine Learning
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    Chapter 41 Explainable Artificial Intelligence (XAI): Motivation, Terminology, and Taxonomy
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Title
Machine Learning for Data Science Handbook
Published by
Springer International Publishing, June 2023
DOI 10.1007/978-3-031-24628-9
ISBNs
978-3-03-124627-2, 978-3-03-124628-9
Editors

Rokach, Lior, Maimon, Oded, Shmueli, Erez

X Demographics

X Demographics

The data shown below were collected from the profiles of 4 X users who shared this research output. Click here to find out more about how the information was compiled.
Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 16 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 16 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 2 13%
Researcher 1 6%
Lecturer 1 6%
Unknown 12 75%
Readers by discipline Count As %
Computer Science 3 19%
Agricultural and Biological Sciences 1 6%
Earth and Planetary Sciences 1 6%
Unknown 11 69%