Association Rule : Unsupervised Machine Learning in Python
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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.
