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Association Rules Analysis
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Association Rules Analysis

Master unsupervised learning techniques with focus on association rules and outlier detection using Python.

Course Cost

Free course

Intermediate

Skill Level

21 Hours

Self-paced lessons

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 Analysis with Python 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.

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What you'll learn

  • Implement association rule mining techniques

  • Apply Apriori and FP Growth algorithms

  • Detect outliers using statistical methods

  • Analyze transactional data patterns

  • Develop constraint-based mining solutions

Skills you'll gain

Association Rule Mining
Frequent Pattern Analysis
Apriori Algorithm
FP Growth
Outlier Detection
Python Programming
Data Analysis
Unsupervised Learning
Pattern Recognition
Statistical Analysis

This course includes:

1.2 Hours PreRecorded video

4 quizzes, 1 assignment

Access on Mobile, Tablet, Desktop

FullTime access

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Certificate

Get a Completion Certificate

Share your certificate with prospective employers and your professional network on LinkedIn.

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PROVIDED BY

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Top companies offer this course to their employees

Top companies provide this course to enhance their employees' skills, ensuring they excel in handling complex projects and drive organizational success.

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There are 5 modules in this course

This comprehensive course focuses on unsupervised learning methods, specifically association rules and outlier detection. Students learn to discover patterns in transactional data through frequent itemset mining and association rule analysis. The curriculum covers essential algorithms like Apriori and FP Growth, along with practical applications in retail and fraud detection. Through hands-on case studies and interactive tutorials, participants gain expertise in implementing these techniques using Python, making them well-equipped for real-world data analysis challenges.

Frequent Itemsets

Module 1 · 3 Hours to complete

Association Rule Mining

Module 2 · 37 Minutes to complete

Apriori and FP Growth Algorithm

Module 3 · 8 Hours to complete

Outliers

Module 4 · 4 Hours to complete

Case Study

Module 5 · 5 Hours to complete

Fee Structure

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Faculties

These are the expert instructors who will be teaching you throughout the course. With a wealth of knowledge and real-world experience, they're here to guide, inspire, and support you every step of the way. Get to know the people who will help you reach your learning goals and make the most of your journey.

Association Rules Analysis

Intermediate

Skill Level

21 Hours

Self-paced lessons

Course Cost

Free course

Completion

CERTIFICATE

Frequently asked Questions

Below are some of the most commonly asked questions about this course. We aim to provide clear and concise answers to help you better understand the course content, structure, and any other relevant information. If you have any additional questions or if your question is not listed here, please don't hesitate to reach out to our support team for further assistance.