Machine Learning and AI Foundations: Clustering and Association
With Keith McCormick
Liked by 1,254 users
Duration: 3h 33m
Skill level: Intermediate
Released: 5/16/2018
Course details
Unsupervised learning is a type of machine learning where algorithms parse unlabeled data. The focus is not on sorting data into known categories but uncovering hidden patterns. Unsupervised learning plays a big role in modern marketing segmentation, fraud detection, and market basket analysis. This course shows how to use leading machine-learning techniques—cluster analysis, anomaly detection, and association rules—to get accurate, meaningful results from big data.
Instructor Keith McCormick reviews the most common clustering algorithms: hierarchical, k-means, BIRCH, and self-organizing maps (SOM). He uses these algorithms for anomaly detection, with additional specialized functions available in IBM SPSS Modeler, and goes over how HDBSCAN works. He closes the course with a review of association rules and sequence detection, and provides some resources for learning more.
All exercises are demonstrated in IBM SPSS Modeler and IBM SPSS Statistics, but the emphasis is on concepts.
Skills you’ll gain
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Showcase on your LinkedIn profile under “Licenses and Certificate” section
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Meet the instructor
Learner reviews
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Rohini Kondaveeti
Rohini Kondaveeti
Student at Velagapudi Ramakrishna Siddhartha Engineering College
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Jason Sjöbeck
Jason Sjöbeck
Experienced Technology Leader: boosting revenue, reducing expenses, improving CSAT via sustainable solutions. Recently 13x. Skilled in Finance…
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Contents
What’s included
- Practice while you learn 1 exercise file
- Test your knowledge 7 quizzes
- Learn on the go Access on tablet and phone
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