Rokach L. Machine Learning for Data Science Handbook 3ed 2023 torrent

Textbook in PDF format

This book is a major update to the very successful first and second editions (2005 and 2010) of Data Mining and Knowledge Discovery Handbook. Since the last edition, this field has continued to evolve and to gain popularity. Existing methods are constantly being improved and new methods, applications and aspects are introduced. The new title of this handbook and its content reflect these changes thoroughly. Some existing chapters have been brought up to date. In addition to major revision of the existing chapters, the new edition includes totally new topics, such as: deep learning, explainable AI, human factors and social issues and advanced methods for big-data. The significant enhancement to the content reflects the growth in importance of data science. The third edition is also a timely opportunity to incorporate many other changes based on peers and students’ feedback. This comprehensive handbook also presents a coherent and unified repository of data science major concepts, theories, methods, trends, challenges and applications. It covers all the crucial important machine learning methods used in data science. Today's accessibility and abundance of data make data science matters of considerable importance and necessity. Given the field's recent growth, it's not surprising that researchers and practitioners now have a wide range of methods and tools at their disposal. While statistics is fundamental for data science, methods originated from artificial intelligence, particularly machine learning, are also playing a significant role. Today's accessibility and abundance of data make data science matters of considerable importance and necessity. Given the field's recent growth, it's not surprising that researchers and practitioners now have a wide range of methods and tools at their disposal. While statistics is fundamental for data science, methods originated from artificial intelligence, particularly machine learning, are also playing a significant role.
Data Science and Knowledge Discovery Using Machine Learning Methods
Handling Missing Attribute Values
Data Integration Process Automation Using Machine Learning: Issues and Solution
Rule Induction
Nearest-Neighbor Methods: A Modern Perspective
Support Vector Machines
Empowering Interpretable, Explainable Machine Learning Using Bayesian Network Classifiers
Soft Decision Trees
Quality Assessment and Evaluation Criteria in Supervised Learning
Trajectory Clustering Analysis
Clustering High-Dimensional Data
Fuzzy C-Means Clustering: Advances and Challenges (Part II)
Clustering in Streams
Introduction to Deep Learning
Graph Embedding
Autoencoders
Generative Adversarial Networks
Spatial Data Science
Multimedia Data Learning
Web Mining
Mining Temporal Data
Cloud Big Data Mining and Analytics: Bringing Greenness and Acceleration in the Cloud
Multi-Label Ranking: Mining Multi-Label and Label Ranking Data
Reinforcement Learning for Data Science
Adversarial Machine Learning
Ensembled Transferred Embeddings
Data Mining in Medicine
Recommender Systems
Activity Recognition
Social Network Analysis for Disinformation Detection
Online Propaganda Detection
Interpretable Machine Learning forFinancial Applications