Description for Cloud Machine Learning Engineering and MLOps
Document Retrieval and Similarity Metrics: Develop a document retrieval system that employs k-nearest neighbors and determines a variety of similarity metrics for text data in order to improve the efficacy of searches.
Clustering using MapReduce and k-means: Utilize k-means to implement document clustering by topic and parallelize the process using MapReduce to ensure scalability.
Methods of Probabilistic Clustering: Investigate probabilistic clustering using mixture models and fit a Gaussian mixture model using expectation maximization (EM).
Innovative Modeling Methodologies: Apply Gibbs sampling to derive inferences for non-convex optimization and perform mixed membership modeling with latent Dirichlet allocation (LDA).
Level: Intermediate
Certification Degree: Yes
Languages the Course is Available: 21
Offered by: On Coursera provided by Duke University
Duration: 3 weeks at 4 hours a week
Schedule: Flexible
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