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

Topics Covered

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Topics Covered

  • What is data mining?
  • Where can it be applied?
  • How do simple classification algorithms work?
  • What are their strengths and weaknesses?
  • In what ways are real-life classification methods more complex?
  • How should you evaluate a classifier’s performance?
  • What is “overfitting” and how can you combat it?
  • How can ensemble techniques combine the result of different algorithms?
  • What ethical considerations arise when mining data?

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Reviews summary

Straightforward data mining overview

This course teaches the very basics of data mining conceptualizing what it is and where it can be used and doing so with no programming required. The course makes an effort to ensure learners have basic statistics knowledge before beginning.
Basic data mining explained
"What is data mining?"
"Where can it be applied?"
"How do simple classification algorithms work?"
Introductory course
"It involves no computer programming, although you need some experience with using computers for everyday tasks."
"High school maths should be more than enough and you’ll need an understanding of some elementary statistics concepts"

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Learners who complete Data Mining with Weka will develop knowledge and skills that may be useful to these careers:

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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.
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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.
Provides a practical guide to data mining, with a focus on using Weka. It covers a wide range of data mining tasks, including data preprocessing, feature selection, model building, and evaluation.
Provides a practical guide to using Python for machine learning. It covers a wide range of topics, including data preprocessing, feature selection, model building, and evaluation.
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Provides a comprehensive overview of deep learning algorithms and techniques, with a focus on using Python. It covers a wide range of topics, including data preprocessing, feature selection, model building, and evaluation.
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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