Association Rule : Unsupervised Machine Learning in Python

Association Rule : Unsupervised Machine Learning in Python, available at $39.99, has an average rating of 4, with 17 lectures, 1 quizzes, based on 14 reviews, and has 1035 subscribers.

You will learn about Describe the input and output of a Association Rule Learning Prepare data with feature engineering techniques Implement Apriori algorithm, Eclat algorithm and FP-growth algorithm Learn the concepts of Support, Confidence and Lift and compute them This course is ideal for individuals who are Beginners starting out to the field of Machine Learning. or Industry professionals and aspiring data scientists. or People who want to know how to write their Association Rule Learning code. It is particularly useful for Beginners starting out to the field of Machine Learning. or Industry professionals and aspiring data scientists. or People who want to know how to write their Association Rule Learning code.

Enroll now: Association Rule : Unsupervised Machine Learning in Python

Summary

Title: Association Rule : Unsupervised Machine Learning in Python

Price: $39.99

Average Rating: 4

Number of Lectures: 17

Number of Quizzes: 1

Number of Published Lectures: 17

Number of Published Quizzes: 1

Number of Curriculum Items: 18

Number of Published Curriculum Objects: 18

Original Price: ₹799

Quality Status: approved

Status: Live

What You Will Learn

Describe the input and output of a Association Rule Learning
Prepare data with feature engineering techniques
Implement Apriori algorithm, Eclat algorithm and FP-growth algorithm
Learn the concepts of Support, Confidence and Lift and compute them

Who Should Attend

Beginners starting out to the field of Machine Learning.
Industry professionals and aspiring data scientists.
People who want to know how to write their Association Rule Learning code.

Target Audiences

Beginners starting out to the field of Machine Learning.
Industry professionals and aspiring data scientists.
People who want to know how to write their Association Rule Learning code.

Artificial intelligence and machine learning are touching our everyday lives in more-and-more ways. There’s an endless supply of industries and applications that machine learning can make more efficient and intelligent. This course introduces you to one of the prominent modelling families of Unsupervised Machine Learning called Association Rule Learning. Association rule mining helps find exciting connections and linkages among large data items. The association rule learning is employed in Market Basket analysis, Web usage mining, Continuous production, Customer analytics, Catalogue design, Shop layout, Recommender systems etc. Association rules are critical in data mining for analyzing and forecasting consumer behaviour. This course provides the learners with the foundational knowledge to use Association Rule Learning to create insights. You will become familiar with the most successful and widely used Association Rule techniques, such as:

Apriori algorithm
Eclat algorithm
FP-growth algorithm

You will learn how to train Association Rule models to find the connections between the data and compute the metrics such as Support, Confidence and Lift. By the end of this course, you will be able to build machine learning models to make Association Rules using your data. The complete Python programs and datasets included in the class are also available for download. This course is designed most straightforwardly to utilize your time wisely. Get ready to do more learning than your machine!

Happy Learning.

Career Growth:

Employment website Indeed has listed machine learning engineers as #1 among The Best Jobs in the U.S., citing a 344% growth rate and a median salary of $146,085 per year. Overall, computer and information technology jobs are booming, with employment projected to grow 11% from 2019 to 2029.

Course Curriculum

Chapter 1: Introduction

Lecture 1: Introduction

Lecture 2: Artificial Intelligence

Lecture 3: Machine Learning

Lecture 4: Supervised Learning

Lecture 5: Supervised Learning: Classifications

Lecture 6: Supervised Learning: Regressions

Lecture 7: Unsupervised Learning

Lecture 8: Unsupervised Learning : Clustering

Lecture 9: Unsupervised Learning : Association

Lecture 10: Installation of Python Platform

Chapter 2: Building and Evaluating Association Rule Learning Models

Lecture 1: Antecedent and Consequent

Lecture 2: Support

Lecture 3: Confidence

Lecture 4: Lift

Lecture 5: Apriori Algorithm

Lecture 6: Eclat Algorithm

Lecture 7: FP-growth Algorithm

Instructors

Karthik Karunakaran, Ph.D.
Transforming Real-World Problems with the Power of AI-ML

Rating Distribution

1 stars: 0 votes
2 stars: 1 votes
3 stars: 4 votes
4 stars: 5 votes
5 stars: 4 votes

Frequently Asked Questions

How long do I have access to the course materials?

You can view and review the lecture materials indefinitely, like an on-demand channel.

Can I take my courses with me wherever I go?

Definitely! If you have an internet connection, courses on Udemy are available on any device at any time. If you don’t have an internet connection, some instructors also let their students download course lectures. That’s up to the instructor though, so make sure you get on their good side!

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