Emily Fox
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 …
Instructor Details
Courses : 2
Specification: Machine Learning: Clustering & Retrieval
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50 reviews for Machine Learning: Clustering & Retrieval
Add a review
| Price | Free |
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| Provider | |
| Duration | 46 hours |
| Year | 2016 |
| Language | Arabic ... |
| Certificate | Yes |
| Quizzes | No |
FREE
Somu P –
Excellent course, which gives you all you need to learn about machine learning. Concepts and hands on practical ex
Manoj K –
session was very helpful & full with relevant contents
Martin R –
I’d bring the last summary video at the beginning (the great summary of all weeks of the course). This would outline the course evolution in advance and give guidance what’s ahead. IMHO this would help to not get lost when drill down in a single section.
Xue –
Great but hard~!
Big O –
More detail on theory behind LDA and HMMs would have been useful. Otherwise, another brilliant course!
PRAVEEN R U –
Nice content and well made presentations.
KAI N –
Excellent course with great and reachable explanation
Jay K S –
Excellent course material and fantastic delivery. You guys made this complex learning so simple and interesting . Thanks for all this, keep the good works.
Srinivas C –
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.
Vikash S N –
It was great but I was also interested to implement the solutions with pyspark…though I did it eventually. Thank you!
Zhongkai M –
Great assignments : )
Edwin P –
Excellent, good contribution to the technical and practical knowledge ML
Jialie ( Y –
The course is really helpful, though it would be better for teacher to illustrate the concepts by using examples, instead of abstract terminologies
Sathiraju E –
Very nice course. Things are well explained, however some concepts could be expanded more.
Akash G –
Machine Learning: Clustering & Retrieval good and learn easily
Martin B –
Greatly enjoyed it. As with the other courses in this specialization the discussion of the subjects is impeccable, especially if you’ve taken some preparatory mathematics courses. The reliance on Graphlab Create is a drag though.
kripa s –
One of the best training experience…
Dennis S –
Amazing course. The Instructors did an awesome job of preparing and presenting the material. I think there is no better and more approachable in–depth course out there. Thank you so much!
YASHKUMAR R T –
Awesome course to understand the concept behind Gaussian Mixture model.
Dohyoung C –
Fascinating course& LDA is little bit difficult to understand, but K–mean and Mixture models are easy to understand and quite important for clustering..
Dimitrios Z –
It has intresting theory but I believe the exercises need to be improvised. Maybe using Jupyter online and guiding the student to write code to solve the problems. In conclusion, I understood the basic theory but mostly that.
Aakash S –
Such a clear explanation of topics of clustering. Without doubt one of the best in business.
Mohamed A H –
A very rich of useful materials course. The instructor has a fantastic explanation ability. The course is pretty organized and the assignments solidifies the understanding of the concepts well. It was an amazing experience!
Christopher M –
Doesn’t go quite as deep into the details as some of the other Machine Learning courses from the University of Washington do. Overall though, the course covers a LOT of ground. and provides exposure to many different topics. I would have liked to have seen an Optional section on the derivation of some of the math that we were given functions for on the Expectation Maximization section. The models in the hierarchical clustering section take longer to fit than is necessary for a course like this (more than 40 times as long as the instructions say it should take), maybe a larger tolerance for convergence should be specified?
Jafed E –
I enjoy the lectures. The professor has a good speaking and teaching style which keeps me interested. Lots of concrete math examples which make it easier to understand. Very good slides which are well formulated and easy to understand
akashkr1498 –
I like the course very much. I learnt so many advance concept and real life implementation.. but slightly disappointed by the quiz question please be specific what you wanted us to answer. looking forward for SVM and deep learning material.
Yufeng X –
It opened the door to more advanced techniques.
Pavan B –
Few concepts were covered in hurry with lot of concepts described abruptly. It took a while for me to do research about those topics to catchup.
Banka C G –
Its my great experience for step by step modules
Shuyi C –
I think it is easy to understand and good to practice. Nice entry level course!
leonardo d –
Awesome course. It was great to learn modern tools in machine learning, not just to apply some black–box on data. I also loved the applications that were showed: it is fantastic to see the algorithms in action, knowing how everything works inside. Another exiting ingredient was how the teachers show you the advantages and weaknesses of each method, as well as the suitable places were they can be applied, or even the most popular extensions or alternatives. I was really really great to had spent those months understanding machine learning in this course and during this favoluos entire specialization.
Muhammad Z H –
Machine Learning: Clustering & Retrieval, I have learned a lot professor
Hanna L –
Great class!
Manuel A –
Great course and specialization overall, both lectures and assignments
Yao X –
Wish to have more detail on implementing the algorithm. Assignments are too easy for understanding the knowledge behind the scene.
Parab N S –
Excellent course on clustering & retrieval by University of Washington
Muhammad W K –
A great course to get the grass–root level understanding of Clustering and Retrieval tasks and going beyond to Unsupervised learning and the core concepts related to it. And starting from the basics all the way to some of the advanced algorithms and models used in the world today. It is simply awesome!
Jayant S –
The course was very detailed. The case study technique was rather very helpful as compared to theoretical technique. I would consider the programming assignments from medium to hard difficulty. The course could have been much better if graphlab as well as scikit coding would have been taught side by side.
Neemesh J –
Coursera is the best learning app. I am really thankful for getting very good training lectures.
Prabhu –
Very clear explanation of concepts with a good selection of examples.
Sayan B –
This is actually a tremendous course. Assignments are not so good, but the materials are wonderful.
Velpula M K –
Good and best to learn.
Nelson P –
Excellent course. I liked the material and the assignments are great to consolidate the learning. I really liked the recap videos to solidify even more what I learned.
Anurag –
Great Experience
Jie S –
Overall, this course covers a lot of materials on clustering methods and algorithms. The assignment instructions are well–prepared. One thing I was struggling with is that I had to install Turicreate on a Linux system, which was a painful experience. It is recommended that the course team advise on how to install Turicreate less painfully on a Windows machine. Other than this, I think it was a great course!
Nick S –
The videos are great, well–structured and introduce gradually the complexity. This is a good idea to explore both methodological and computation aspects of clustering. Unfortunately, the exercises requires the use of a specific library, instead of scikit–learn and numpy. Furthermore, they also required Python 2, while Python 3 is now widely used.
parag_verma –
Thanks to the entire team of this course.
Manish G –
This topic was very deep and I learnt many complex algos. Would suggest to have some more examples for the algorithms presented in this modules.
Dario D G –
Organized decently, yet tools such as TuriCreate have been associated to a lot of problems with running the assignments. Additionally, it seemed very difficult to receive any sort of assistance if stuck with an assignment or tool.
Subba R V O –
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.