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Unsupervised Algorithms in ML

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The course covers the fundamentals of unsupervised learning methods and their real-world applications, particularly recommender systems.

Key AI Functions:cluster analysis,dimensionality reduction,unsupervised learning,recommender systems,matrix factorization,ai_machine_learning,ai & machine learning

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Description for Unsupervised Algorithms in ML

Unsupervised Learning Fundamentals: Gain an understanding of the definition of unsupervised learning and the methods employed to identify concealed patterns in unlabeled data.
Matrix Factorization Algorithms: Investigate a variety of matrix factorization methods and comprehend the function of each algorithm in the context of machine learning.
Dimensionality Reduction and Clustering: Investigate the application of unsupervised learning techniques in real-world scenarios to reduce dimensionality and cluster data.
Recommender Systems: Acquire practical experience with product recommendation algorithms and their implementation in real-world recommender systems.

Level: Intermediate

Certification Degree: Yes

Languages the Course is Available: 21

Offered by: On Coursera provided by University of Colorado Boulder

Duration: 38 hours (approximately)

Schedule: Flexible

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