Cluster Analysis, Association Mining, and Model Evaluation
Learn essential data mining techniques including clustering, market basket analysis, and model performance evaluation.
Learn essential data mining techniques including clustering, market basket analysis, and model performance evaluation.
This course cannot be purchased separately - to access the complete learning experience, graded assignments, and earn certificates, you'll need to enroll in the full Data Science Fundamentals Specialization program. You can audit this specific course for free to explore the content, which includes access to course materials and lectures. This allows you to learn at your own pace without any financial commitment.
4.6
(39 ratings)
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Instructors:
English
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What you'll learn
Apply cluster analysis for market segmentation
Implement collaborative filtering and association rules
Evaluate classification model performance
Develop regression-based prediction models
Understand supervised vs unsupervised learning applications
Skills you'll gain
This course includes:
0.1 Hours PreRecorded video
2 quizzes
Access on Mobile, Tablet, Desktop
FullTime access
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There are 4 modules in this course
This comprehensive course explores key data mining techniques and evaluation methods. Students learn cluster analysis for market segmentation, collaborative filtering for recommendation systems, and association rules mining for market basket analysis. The curriculum covers both classification and regression-type prediction models, with emphasis on performance evaluation and practical applications across different industries.
Cluster Analysis and Segmentation
Module 1 · 1 Hours to complete
Collaborative Filtering, Association Rules Mining (Market Basked Analysis)
Module 2 · 42 Minutes to complete
Classification-Type Prediction Models
Module 3 · 59 Minutes to complete
Regression-Type Prediction Models
Module 4 · 1 Hours to complete
Fee Structure
Instructor
Assistant Director of Technology Programs
Julie Pai is an accomplished professional with over 10 years of experience in technology education programs. Her career began with a strong foundation in biology and clinical research, which ignited her interest in statistical analysis and data analysis. As the Assistant Director of Technology Programs at the University of California, Irvine, she collaborates with industry experts to design, launch, and manage a variety of courses in the technology sector, including Data Science, Data Analytics, Cloud Computing, and Machine Learning.Her technical expertise encompasses a range of tools and programming languages such as Tableau, SQL, MySQL, Python, Spark, Hive, and Scala. Julie is dedicated to enhancing educational offerings in technology and has developed courses that cover essential topics like predictive modeling and natural language processing. Her commitment to bridging the gap between academia and industry ensures that learners receive relevant and practical training in today's fast-evolving tech landscape.
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4.6 course rating
39 ratings
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