Explore

Cluster Analysis, Association Mining, and Model Evaluation

Approx. 4 hours to complete

Save Course

Go to Course

Course Summary

Learn about data mining and machine learning techniques such as clustering, association analysis, and model evaluation in this course.

Key Learning Points

Understand the basics of data mining and machine learning techniques.
Discover the principles behind clustering and association analysis.
Learn how to evaluate models and interpret their results.
Explore real-world applications of data mining and machine learning.

Learning Outcomes

Gain a solid understanding of data mining and machine learning techniques.
Develop the ability to apply clustering and association analysis to real-world problems.
Learn how to evaluate models and interpret their results.

Prerequisites or good to have knowledge before taking this course

Basic knowledge of statistics and probability.
Familiarity with programming concepts and languages such as Python.

Course Difficulty Level

Intermediate

Course Format

Online
Self-paced
Video lectures

Similar Courses

Applied Data Science: Machine Learning
Data Mining with Weka
Data Science Essentials

Related Education Paths

Notable People in This Field

Pedro Domingos
Yann LeCun

Related Books

Description

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.

Knowledge

Cluster analysis and segmentation
Collaborative filtering and market basket analysis
Applications of classification- and regression-type prediction models

Outline

Cluster Analysis and Segmentation
Cluster Analysis and Segmentation
Supplemental Resources

Collaborative Filtering, Association Rules Mining (Market Basked Analysis)
Market Basket Analysis
Collaborative Filtering, Association Rules Mining (Market Basket Analysis)
Modules 1 and 2

Classification-Type Prediction Models
Evaluating Model Performance
Classification-Type Prediction Models
Supplemental Readings

Regression-Type Prediction Models
Regression-Type Prediction Models
Modules 3 and 4

Summary of User Reviews

Find out what users are saying about Cluster Analysis, Association Mining, and Model Evaluation. Discover how this course can help you learn and improve your skills. Read reviews and ratings from past students.

Key Aspect Users Liked About This Course

Many users found the course content to be comprehensive and well-organized.

Pros from User Reviews

The course covers a wide range of topics related to data clustering, association mining, and model evaluation.
The instructors are knowledgeable and provide clear explanations.
The assignments and quizzes are challenging but rewarding, allowing students to apply what they've learned in a practical way.
The course is self-paced, allowing students to complete it on their own schedule.
The course materials are well-designed and easy to follow.

Cons from User Reviews

Some users felt that the course was too technical and difficult for beginners.
A few users reported technical difficulties with the Coursera platform.
The course may not be suitable for those looking for a more theoretical approach to the subject matter.
Some users found the course to be too time-consuming.
The course does not provide hands-on experience with specific software tools.

Recommended for you

Process Mining: Data science in Action

Process mining is the missing link between model-based process analysis and data-oriented analysis techniques. It is not sufficient to focus on data storage and data analysis....

Save Course

Predictive Analytics and Data Mining

This course introduces students to the science of business analytics while casting a keen eye toward the artful use of numbers found in the digital space....

Save Course