Get updates on Eligibility, Admission, Placements Fees Structure
Quick Facts
| Medium Of Instructions | Mode Of Learning | Mode Of Delivery |
|---|---|---|
| English | Self Study | Video and Text Based |
Course Overview
Machine Learning: Clustering & Retrieval is the University of Washington-offered online certification programme. The online course will expose the students to the practical understanding of clustering and retrieval aspects of machine learning through the case study of Finding Similar Documents. Machine Learning: Clustering & Retrieval Certification Course facilitates the students to take the course completely in online mode from anywhere in the world.
Provided by Coursera, Machine Learning: Clustering & Retrieval Certification Syllabus will discuss the similarity-based algorithms for retrieval, latent Dirichlet allocation (LDA), clustering, and the implementation of expectation maximization (EM) and the like. Machine Learning: Clustering & Retrieval Certification by Coursera is the fourth and last course in the Machine Learning Specialization.
The Highlights
- Provided by Coursera
- Offered by the University of Washington
- Self-Paced Learning Option
- 100% Online Course
- Around 17 Hours to Complete
- Flexible Deadlines
- Shareable Certificate
- Financial Aid Available
Programme Offerings
- English videos with multiple subtitles
- Shareable Certificate
- Financial aid available
- Shareable Certificates
- Self-Paced Learning Option
- Course Videos & Readings
- Practice
Courses and Certificate Fees
| Certificate Availability | Certificate Providing Authority |
|---|---|
| yes | Coursera |
Coursera provides the candidates of the online course with two options to join the online course, namely, the audit and purchasing mode. Machine Learning: Clustering & Retrieval Certification Fee will vary as per the number of months the learners want to stay on the course and learn. Coursera also provides the EMI payment option and 14-day refund period for the students.
Machine Learning: Clustering & Retrieval Fee Structure
Duration | Amount in INR |
1 Month | INR 4,091 |
What you will learn
After the completion of the Machine Learning: Clustering & Retrieval Training, the students will be able to use k-nearest neighbours to make a document retrieval system, to understand supervised and unsupervised learning tasks, use MapReduce for the parallelization of k-means, to know the similarity metrics for text data and many more.
Who it is for
Machine Learning: Clustering & Retrieval Classes is a suitable online course for the professionals like ML Engineer, Data Engineer and whatnot.
Admission Details
Step 1 -Register and sign in at https://www.coursera.org
Step 2 -Find the online programme ‘Machine Learning: Clustering & Retrieval’ provided by the University of Washington.
Step 2 -Choose the option ‘Enrol’ and start taking the course.
The Syllabus
Videos
- Welcome and introduction to clustering and retrieval tasks
- Course overview
- Module-by-module topics covered
- Assumed background
Readings
- Important Update regarding the Machine Learning Specialization
- Slides presented in this module
- Software tools you'll need for this course
- A big week ahead!
- Get help and meet other learners. Join your Community!
Videos
- Retrieval as k-nearest neighbor search
- 1-NN algorithm
- k-NN algorithm
- Document representation
- Distance metrics: Euclidean and scaled Euclidean
- Writing (scaled) Euclidean distance using (weighted) inner products
- Distance metrics: Cosine similarity
- To normalize or not and other distance considerations
- Complexity of brute force search
- KD-tree representation
- NN search with KD-trees
- Complexity of NN search with KD-trees
- Visualizing scaling behavior of KD-trees
- Approximate k-NN search using KD-trees
- Limitations of KD-trees
- LSH as an alternative to KD-trees
- Using random lines to partition points
- Defining more bins
- Searching neighboring bins
- LSH in higher dimensions
- (OPTIONAL) Improving efficiency through multiple tables
- A brief recap
Readings
- Slides presented in this module
- Choosing features and metrics for nearest neighbor search
- (OPTIONAL) A worked-out example for KD-trees
- Implementing Locality Sensitive Hashing from scratch
Practice Exercises
- Representations and metrics
- Choosing features and metrics for nearest neighbor search
- KD-trees
- Locality Sensitive Hashing
- Implementing Locality Sensitive Hashing from scratch
Videos
- The goal of clustering
- An unsupervised task
- Hope for unsupervised learning, and some challenge cases
- The k-means algorithm
- k-means as coordinate descent
- Smart initialization via k-means++
- Assessing the quality and choosing the number of clusters
- Motivating MapReduce
- The general MapReduce abstraction
- MapReduce execution overview and combiners
- MapReduce for k-means
- Other applications of clustering
- A brief recap
Readings
- Slides presented in this module
- Clustering text data with k-means
Quizzes
- k-means
- Clustering text data with K-means
- MapReduce for k-means
Videos
- Motiving probabilistic clustering models
- Aggregating over unknown classes in an image dataset
- Univariate Gaussian distributions
- Bivariate and multivariate Gaussians
- Mixture of Gaussians
- Interpreting the mixture of Gaussian terms
- Scaling mixtures of Gaussians for document clustering
- Computing soft assignments from known cluster parameters
- (OPTIONAL) Responsibilities as Bayes' rule
- Estimating cluster parameters from known cluster assignments
- Estimating cluster parameters from soft assignments
- EM iterates in equations and pictures
- Convergence, initialization, and overfitting of EM
- Relationship to k-means
- A brief recap
Readings
- Slides presented in this module
- (OPTIONAL) A worked-out example for EM
- Implementing EM for Gaussian mixtures
- Clustering text data with Gaussian mixtures
Quizzes
- EM for Gaussian mixtures
- Implementing EM for Gaussian mixtures
- Clustering text data with Gaussian mixtures
Videos
- Mixed membership models for documents
- An alternative document clustering model
- Components of latent Dirichlet allocation model
- Goal of LDA inference
- The need for Bayesian inference
- Gibbs sampling from 10,000 feet
- A standard Gibbs sampler for LDA
- What is collapsed Gibbs sampling?
- A worked example for LDA: Initial setup
- A worked example for LDA: Deriving the resampling distribution
- Using the output of collapsed Gibbs sampling
- A brief recap
Readings
- Slides presented in this module
- Modeling text topics with Latent Dirichlet Allocation
Quizzes
- Latent Dirichlet Allocation
- Learning LDA model via Gibbs sampling
- Modeling text topics with Latent Dirichlet Allocation
Videos
- Module 1 recap
- Module 2 recap
- Module 3 recap
- Module 4 recap
- Why hierarchical clustering?
- Divisive clustering
- Agglomerative clustering
- The dendrogram
- Agglomerative clustering details
- Hidden Markov models
- What we didn't cover
- Thank you!
Readings
- Slides presented in this module
- Modeling text data with a hierarchy of clusters
Quizzes
- Modeling text data with a hierarchy of clusters
Instructors
UW Washington Frequently Asked Questions (FAQ's)
The online certification programme belongs to the Machine Learning Specialization.
The online certificate course is made available on Coursera by the University of Washington.
The learners can complete the online programme within approximately 17 hours. However, the learners can set their own deadlines as per their schedule and convenience.
Yes, the learners are provided with financial aid if they struggle to make the payment of the fee prescribed by Coursera.
The course is designed and supervised by Emily Fox and Carlos Guestrin who are the Amazon Professors of Machine Learning and