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Updated in [October 16th, 2023]
What does this course tell?
(Please note that the following overview content is from the original platform)
Welcome to Cluster Analysis, Association Mining, and Model Evaluation. In this course we will begin with an exploration of cluster analysis and segmentation, and discuss how techniques such as collaborative filtering and association rules mining can be applied. We will also explain how a model can be evaluated for performance, and review the differences in analysis types and when to apply them.
We considered the value of this course from many aspects, and finally summarized it for you from two aspects: skills and knowledge, and the people who benefit from it:
(Please note that our content is optimized through artificial intelligence tools and carefully reviewed by our editorial staff.)
What skills and knowledge will you acquire during this course?
During this course, the learner will acquire the following skills and knowledge:
1. Cluster Analysis: The learner will understand the concept of cluster analysis and segmentation. They will learn various techniques and algorithms used for clustering data, such as k-means clustering and hierarchical clustering.
2. Segmentation: The learner will learn how to segment data into meaningful groups based on similarities or patterns. They will understand the importance of segmentation in various fields, such as marketing and customer segmentation.
3. Collaborative Filtering: The learner will explore collaborative filtering techniques used in recommendation systems. They will understand how to use user behavior data to make personalized recommendations.
4. Association Rules Mining: The learner will learn how to discover interesting relationships or associations between items in a dataset. They will understand the Apriori algorithm and other techniques used for association rules mining.
5. Model Evaluation: The learner will understand how to evaluate the performance of a model. They will learn various evaluation metrics, such as accuracy, precision, recall, and F1 score. They will also learn techniques for cross-validation and model selection.
6. Analysis Types: The learner will review different types of analysis, such as descriptive analysis, predictive analysis, and prescriptive analysis. They will understand the differences between these types and when to apply them in real-world scenarios.
Who will benefit from this course?
This course will benefit data analysts, data scientists, and researchers who are interested in understanding and applying cluster analysis, association mining, and model evaluation techniques. It will also be useful for professionals working in marketing, customer segmentation, recommendation systems, and data-driven decision making.
