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Ian Witten,
Peter Reutemann,
Jemma König,
Eibe Frank,
and
Mark Hall
Topics Covered
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Topics Covered
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Topics Covered
- Time series analysis
- Data stream mining
- Incremental classifiers
- Evolving data streams
- Support vector machines
- Accessing data mining in R
- Distributed data mining
- Map-reduce framework
- Scripting data mining in Python and Groovy
- Applications: Soil analysis, Sentiment analysis, Bioinformatics, MRI neuroimaging, Image classification
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Provides a comprehensive overview of data mining techniques, including data preprocessing, clustering, classification, association rule mining, and text mining. It valuable resource for students, researchers, and practitioners in the field.
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Provides a practical introduction to data mining techniques, with a focus on machine learning algorithms. It good choice for students and practitioners who want to learn how to apply data mining techniques to real-world problems.
Provides a comprehensive overview of statistical learning methods, including linear and logistic regression, decision trees, support vector machines, and ensemble methods. It valuable resource for students and researchers in the field of data mining.
Provides a comprehensive overview of machine learning techniques, with a focus on deep learning. It valuable resource for students and practitioners who want to learn how to apply machine learning techniques to real-world problems.
Provides a comprehensive overview of data mining techniques for large datasets. It covers topics such as data preprocessing, clustering, classification, association rule mining, and text mining. It valuable resource for students and researchers in the field of data mining.
Provides a comprehensive overview of pattern recognition and machine learning techniques. It covers topics such as supervised learning, unsupervised learning, and reinforcement learning. It valuable resource for students and researchers in the field of data mining.
Provides a comprehensive overview of data mining techniques, with a focus on applications and challenges. It valuable resource for students and practitioners who want to learn how to apply data mining techniques to real-world problems.
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Provides a comprehensive overview of data mining techniques, with a focus on tutorials. It valuable resource for students and practitioners who want to learn how to apply data mining techniques to real-world problems.
Provides a comprehensive overview of data mining techniques, with a focus on business intelligence. It valuable resource for students and practitioners who want to learn how to apply data mining techniques to real-world business problems.
Provides a comprehensive overview of data mining techniques, with a focus on making them accessible to a non-technical audience. It valuable resource for students and practitioners who want to learn about data mining without getting bogged down in the technical details.
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Provides a comprehensive overview of data mining techniques, with a focus on applications using the R programming language. It valuable resource for students and practitioners who want to learn how to apply data mining techniques using R.
Provides a comprehensive overview of data mining techniques, with a focus on using the Python programming language. It valuable resource for students and practitioners who want to learn how to apply data mining techniques using Python.
Provides a comprehensive overview of data mining techniques, with a focus on knowledge discovery. It valuable resource for students and researchers in the field of data mining.
Provides a comprehensive overview of data mining techniques, with a focus on making them accessible to a non-technical audience. It valuable resource for students and practitioners who want to learn about data mining without getting bogged down in the technical details.
This book, written by the Stanford University professor and former Chief Scientist at Baidu, provides a clear overview of machine learning with lots of diagrams and hands-on practice.
Provides a comprehensive and practical guide to deep learning, including hands-on exercises and real-world examples.
Classic text on machine learning and statistical pattern recognition, with a focus on Bayesian approaches. The author has won the prestigious Turing Award.
Provides a balanced treatment of both statistical and machine learning methods, making it accessible to a wide audience.
Practical guide to machine learning with Python, covering a wide range of topics from data preprocessing to model evaluation.
Gentle introduction to machine learning, suitable for those with no prior knowledge of the field, with a focus on understanding the concepts rather than the math.
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