Association Rule Mining: Basic Theory & Practice

Association Rule Mining: Basic Theory & Practice, available at $54.99, has an average rating of 3.85, with 21 lectures, 3 quizzes, based on 45 reviews, and has 6104 subscribers.

You will learn about Basic theory of association rule mining Basic metrics of association rule mining Apriori algorithm Market basket analysis with Python This course is ideal for individuals who are Beginners in association rule mining It is particularly useful for Beginners in association rule mining.

Enroll now: Association Rule Mining: Basic Theory & Practice

Summary

Title: Association Rule Mining: Basic Theory & Practice

Price: $54.99

Average Rating: 3.85

Number of Lectures: 21

Number of Quizzes: 3

Number of Published Lectures: 21

Number of Published Quizzes: 3

Number of Curriculum Items: 24

Number of Published Curriculum Objects: 24

Original Price: $22.99

Quality Status: approved

Status: Live

What You Will Learn

Basic theory of association rule mining
Basic metrics of association rule mining
Apriori algorithm
Market basket analysis with Python

Who Should Attend

Beginners in association rule mining

Target Audiences

Beginners in association rule mining

Welcome to the association rule mining course. This course is an introductory course. You will learn basic knowledge of association rule mining in this course.

Association rule mining is a useful technique to explore associations between variables. It contributes to effective cross-selling and has been applied to construct recommender system in EC sites. We can use it not only in marketing analytics but also other fields in business analytics.

This course intends to provide you with theoretical knowledge as well as python coding. Theoretical knowledge is important to understand the algorithm of data mining, and it can be a useful foundation for more advanced learning.

This course consists of 4 sections. In the first section, you will learn what an association rule is. In Session 2, you will learn the basic metrics of association rule mining. Session 3 covers apriori algorithm that is a useful method to identify important associations between variables. Session 4 is a Hands-On chapter, where you will learn how to implement association rule mining in Python.

I’m looking forward to seeing you in this course!

Source of Pictures:

Course Image: Gerd Altmann from Pixabay
PV:
– Beer: Hans Braxmeie from Pixabay
– Pretzel: Couleur from Pixabay
– Potatoes: RitaE from Pixabay
– Diaper: PublicDomainPictures from Pixabay

Course Curriculum

Chapter 1: What is Association Rule Mining?

Lecture 1: Introduction

Lecture 2: What is Association Rule Mining?

Lecture 3: Comparison with Similar Methods

Lecture 4: Basic Terms

Lecture 5: Basic Math: Set Theory

Chapter 2: Basic Metrics

Lecture 1: Support

Lecture 2: Basic Math: Conditional Probability

Lecture 3: Confidence

Lecture 4: Lift

Lecture 5: Comprehension

Lecture 6: Summary

Chapter 3: Apriori Algorithm

Lecture 1: The Role of Apriori Algorithm

Lecture 2: Apriori Principle

Lecture 3: Apriori Algorithm: Phase 1ーFind Frequent Itemsets

Lecture 4: Apriori Algorithm: Phase 2―Association Rule Selection

Lecture 5: Summary

Chapter 4: Market Basket Analysis with Python

Lecture 1: Dataset

Lecture 2: Data Preparation

Lecture 3: Basic Metrics

Lecture 4: Apriori Algorithm

Chapter 5: Thank you!

Lecture 1: Thank you!

Instructors

Takuma Kimura
Scientist of Organizational Behavior & Business Analytics

Rating Distribution

1 stars: 2 votes
2 stars: 1 votes
3 stars: 2 votes
4 stars: 22 votes
5 stars: 18 votes

Frequently Asked Questions

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