Machine Learning - Clustering and Retrieval, Certificate | Part time online | Coursera | United States
1 months
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About

This Machine Learning - Clustering and Retrieval course offered by Coursera in partnership with University of Washington is part of the Machine Learning Specialization

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Overview

A reader is interested in a specific news article and you want to find similar articles to recommend.  What is the right notion of similarity?  Moreover, what if there are millions of other documents?  Each time you want to a retrieve a new document, do you need to search through all other documents?  How do you group similar documents together?  How do you discover new, emerging topics that the documents cover? The  Machine Learning - Clustering and Retrieval course is offered by Coursera in partnership with University of Washington.

In this third case study, finding similar documents, you will examine similarity-based algorithms for retrieval.  In this course, you will also examine structured representations for describing the documents in the corpus, including clustering and mixed membership models, such as latent Dirichlet allocation (LDA).  You will implement expectation maximization (EM) to learn the document clusterings, and see how to scale the methods using MapReduce.

Learning Outcomes

By the end of this course, you will be able to:

  • Create a document retrieval system using k-nearest neighbors.
  • Identify various similarity metrics for text data.
  • Reduce computations in k-nearest neighbor search by using KD-trees.
  • Produce approximate nearest neighbors using locality sensitive hashing.
  • Compare and contrast supervised and unsupervised learning tasks.
  • Cluster documents by topic using k-means.
  • Describe how to parallelize k-means using MapReduce.
  • Examine probabilistic clustering approaches using mixtures models.
  • Fit a mixture of Gaussian model using expectation maximization (EM).
  • Perform mixed membership modeling using latent Dirichlet allocation (LDA).

Programme Structure

Courses included:

  • Welcome
  • Nearest Neighbor Search
  • Clustering with k-means
  • Mixture Models
  • Mixed Membership Modeling via Latent Dirichlet Allocation
  • Hierarchical Clustering & Closing Remarks

Key information

Duration

  • Part-time
    • 1 months

Start dates & application deadlines

You can apply for and start this programme anytime.

Language

English

Delivered

Online

Academic requirements

We are not aware of any specific GRE, GMAT or GPA grading score requirements for this programme.

English requirements

We are not aware of any English requirements for this programme.

Tuition Fee

To always see correct tuition fees
  • International

    Free
    Tuition Fee
    Based on the tuition of 0 USD for the full programme during 1 months.
  • National

    Free
    Tuition Fee
    Based on the tuition of 0 USD for the full programme during 1 months.

You can choose from hundreds of free courses, or get a degree or certificate at a breakthrough price. You can now select Coursera Plus, an annual subscription that provides unlimited access.

Funding

Coursera provides financial aid to learners who cannot afford the fee. Apply for it by clicking on the Financial Aid link beneath the "Enroll" button on the left. You'll be prompted to complete an application and will be notified if you are approved. You'll need to complete this step for each course in the Specialization, including the Capstone Project.

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