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Ian Witten, Peter Reutemann, Jemma König, Eibe Frank, and Mark Hall

Most FutureLearn courses run multiple times. Every run of a course has a set start date but you can join it and work through it after it starts. Find out more This course is aimed at anyone who deals in data. You should have completed Data Mining with Weka and More Data Mining with Weka – or be an experienced Weka user. Although the course includes some scripting with Python, you need no prior knowledge of the language. You will have to install and configure some software components; we provide full instructions. You can use the hashtag #FLadvanceddatamining to talk about this course on social media.

Topics Covered

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Most FutureLearn courses run multiple times. Every run of a course has a set start date but you can join it and work through it after it starts. Find out more This course is aimed at anyone who deals in data. You should have completed Data Mining with Weka and More Data Mining with Weka – or be an experienced Weka user. Although the course includes some scripting with Python, you need no prior knowledge of the language. You will have to install and configure some software components; we provide full instructions. You can use the hashtag #FLadvanceddatamining to talk about this course on social media.

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 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.

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