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