CS
5.0Reviewed Feb 12, 2020
Excellent Course. This course provides in depth understanding of what's going in the background when an algorithm runs and how we can tune it for our purpose.
Machine Learning: Clustering & Retrieval
Case Studies: Finding Similar Documents 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? 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). -Describe the steps of a Gibbs sampler and how to use its output to draw inferences. -Compare and contrast initialization techniques for non-convex optimization objectives. -Implement these techniques in Python.
Status: Machine Learning Algorithms
Status: Statistical Machine Learning
Course17 hours
Featured reviews
DS
5.0Reviewed Aug 4, 2020
A challenging course!!! It's necessary to fix some compatibility problems with Tury and Windows, because Python 2.7 it's obsolete. I really enjoy it!!!
DP
5.0Reviewed Jan 25, 2017
The material is complex and challenging, but the teaching procedure is carefully thought out in a way that you quickly get it, giving you a great sense of accomplishment.
SC
4.0Reviewed Jan 7, 2019
This was a really good course, It made me familiar with many tools and techniques used in ML. With this in hand I will be able to go out there and explore and understand things much better.
TT
5.0Reviewed Oct 30, 2016
I really learn a lot in this course, although the materials are very difficult at first read, but Emily's explanation were clear and I would be able to get the idea after a few review.
SO
5.0Reviewed Jan 30, 2020
A great course, well organized and delivered with detailed info and examples. The quiz and the programming assignments are good and help in applying the course attended.
KS
5.0Reviewed Jun 30, 2017
I really enjoyed and learned a lot from this class. It made me interested to go out and learn other machine learning methods which are derived from what was taught.
UZ
5.0Reviewed Nov 28, 2016
This was another great course. I hope that the instructors indulge in a little bit more theory. Anyway it was a magnificent course. Hope the coming courses are as good as this one.
JS
5.0Reviewed Jan 5, 2019
Excellent course material and fantastic delivery. You guys made this complex learning so simple and interesting . Thanks for all this, keep the good works.
PJ
5.0Reviewed Oct 28, 2017
A great course to understand clustering as well as text mining. Lectures on KDD and LSH are equally important to understand and implement these algo . Many thanks
AA
4.0Reviewed Apr 10, 2017
Overall is great. The LDA and Dendrograms lack quality/specificity and depth of the previous topics. So sad the Specialization collapsed at 4 courses instead of 6.
RG
5.0Reviewed Sep 9, 2017
Good presentation of topics. Detailed walk through of few advanced topics covered at the end would have been great. Felt the presentation went too fast.