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Ian Witten,
Peter Reutemann,
Jemma König,
and
Eibe Frank
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"
Activities
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Data Mining with Weka.
These are activities you can do either before, during, or after a course.
Career center
Learners who complete Data Mining with Weka will develop knowledge and skills
that may be useful to these careers:
For more career information including salaries, visit:
OpenCourser.com/course/0t514w/data
Reading list
We haven't picked any books for this reading list yet.
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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.
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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 comprehensive overview of machine learning algorithms and techniques, with a focus on using R, a popular open-source programming language for statistical computing. It covers a wide range of topics, including data preprocessing, feature selection, model building, and evaluation.
Provides a comprehensive overview of machine learning algorithms and techniques, with a focus on using Python, a popular programming language for data science. It covers a wide range of topics, 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.
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.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/0t514w/data
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