Association Rule Unsupervised Machine Learning In Python

What you'll 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
Requirements
Basic knowledge of Python Programming
Description
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 algorithmEclat algorithmFP-growth algorithmYou 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.
Overview
Section 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
Section 2: Building and Evaluating Association Rule Learning Models
Lecture 11 Antecedent and Consequent
Lecture 12 Support
Lecture 13 Confidence
Lecture 14 Lift
Lecture 15 Apriori Algorithm
Lecture 16 Eclat Algorithm
Lecture 17 FP-growth Algorithm
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.

Homepage

https://www.udemy.com/course/association/

Association Rule Unsupervised Machine Learning In Python Fast Download
Association Rule Unsupervised Machine Learning In Python Full Download