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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	UW WashingtonCoursera

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 3,992

What you will learn

Machine learning

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 EngineerData Engineer

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!

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

Practice Exercises

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

Practice Exercises

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

Practice Exercises

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

Practice Exercise

Modeling text data with a hierarchy of clusters

Instructors

UW Washington Frequently Asked Questions (FAQ's)

1: Which specialization does the Machine Learning: Clustering & Retrieval online course belong to?

The online certification programme belongs to the Machine Learning Specialization.

2: Which institution makes the Machine Learning Specialization online certification available on Coursera?

The online certificate course is made available on Coursera by the University of Washington.

3: How many hours do the learners require at the least to duly complete the online course?

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.

4: Does Coursera render the learners who have difficulties in paying the fee financial assistance?

Yes, the learners are provided with financial aid if they struggle to make the payment of the fee prescribed by Coursera.

5: Who did design and supervise the online course?

The course is designed and supervised by Emily Fox and Carlos Guestrin who are the Amazon Professors of Machine Learning and