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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
Read more
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
Read more
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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Reviews summary
Mastering advanced weka & data mining
According to students, this is a largely positive course that provides a deep dive into advanced data mining concepts using Weka. Learners particularly highlight the practical applications and hands-on examples, finding the Python scripting integration highly useful for real-world scenarios. While the course delivers solid and current content, especially in areas like time series and data stream analysis, some find the pace very fast and feel it assumes extensive prior knowledge beyond the stated prerequisites. The R and Groovy sections receive mixed feedback, with some wishing for more depth. Overall, it's highly recommended for experienced Weka users seeking to advance their skills.
Python scripting is praised, but R and Groovy sections are less developed.
"The Python scripting parts were a great bonus, making it practical and a good addition."
"Specifically, the R and Groovy sections felt tacked on and rushed, lacking the depth of other modules."
"I had hoped for more in-depth coding examples, especially for the more complex aspects presented in the course."
Content and tools are generally current, with updates noted.
"My only minor gripe is that some of the Weka interfaces shown in the videos seemed slightly outdated compared to the latest version, but it wasn't a major hindrance."
"A minor point: some of the older reviews mention outdated interfaces, but I didn't experience that significantly in the current run."
"I found the course material to be current and the examples relevant to real-world scenarios."
Emphasizes real-world applications and hands-on learning.
"The practical applications covered, like sentiment analysis, were very well illustrated."
"I appreciated the hands-on examples, though sometimes the pace was a bit too fast for me to grasp everything without re-watching."
"I can immediately apply these skills to my work, which is invaluable."
Provides deep insights into complex data mining techniques.
"The course content is highly relevant for anyone looking to deepen their understanding of data mining, especially with Weka."
"This course truly pushes me beyond basic Weka usage. I've been using Weka for years, and this still taught me new, valuable techniques."
"I found the content solid, though it demanded a strong prior understanding of Weka and data mining concepts."
The course can be fast-paced and demands strong prior knowledge.
"While the topics covered are advanced, I felt some parts of the course assumed too much prior knowledge beyond the stated prerequisites."
"The pace was too fast, and the explanations were insufficient for the 'advanced' nature of the topics. I found myself constantly looking for external resources."
"I completed the 'More Data Mining with Weka' course and still felt overwhelmed by some of the content."
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Reading list
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Provides a comprehensive introduction to data mining with a focus on practical tools and techniques, particularly using the Weka software. It widely used textbook and a good resource for understanding the practical aspects of applying data mining algorithms.
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 thorough introduction to the fields of pattern recognition and machine learning, with a strong emphasis on a Bayesian perspective. It is suitable for advanced undergraduates and graduate students and is considered a foundational text in the field, offering a deep dive into the theoretical underpinnings relevant to 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 practical approach to learning data mining using the R programming language. It emphasizes hands-on experience and is suitable for those who want to apply data mining techniques using R. It covers a wide range of algorithms with practical examples.
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.
Provides a comprehensive introduction to the fundamental concepts, principles, and techniques of data mining. It is widely used as a textbook in academic institutions and serves as a valuable reference for both students and professionals seeking a broad understanding of the field. It covers a wide range of topics, including data preprocessing, mining frequent patterns, classification, clustering, and outlier detection.
Offers a clear and accessible introduction to the core concepts and algorithms in data mining. It is suitable for those new to the field and requires only a modest background in mathematics. It covers fundamental topics and provides numerous examples to illustrate each concept, making it a good starting point for gaining a broad understanding.
This classic and highly-regarded book that bridges the gap between statistics and machine learning with a strong focus on data mining. While mathematically rigorous, it provides a comprehensive overview of key algorithms and concepts. It is an excellent resource for deepening understanding and is often used in graduate-level courses.
Focuses on the techniques for mining data from the web and other massive datasets. It is particularly relevant for understanding contemporary data mining challenges related to big data. It covers topics such as link analysis, social network analysis, and recommendation systems, making it valuable for those interested in large-scale data mining applications.
Provides a business-oriented introduction to data mining and data science. It focuses on the fundamental principles of data science and how to think analytically about data to solve business problems. It's an excellent resource for understanding the practical applications and business value of data mining techniques.
Practical guide to developing predictive models, covering the entire modeling process with a focus on real-world examples and R code. It is highly valuable for practitioners and students looking to apply data mining techniques to build predictive models.
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