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Borys Zibrov profile image
Borys Zibrov profile image
10/10 starsCompleted
  • 9 reviews
  • 8 completed
2 years, 9 months ago
This is course #4 (out of 4) in the Machine Learning specialization from University of Washington on Coursera. Yes, there were 5 courses initially and a capstone project but the last two were removed. Seems ... This is course #4 (out of 4) in the Machine Learning specialization from University of Washington on Coursera. Yes, there were 5 courses initially and a capstone project but the last two were removed. Seems to me like this was related to Dato (Turi) acquisition by Apple but I don't know if there was any announcement. So, if I were to rate the whole specialization I would have taken one star out for that. However, I liked this course as much as the other 3. I've already written reviews for #1 and #2, so perhaps it makes more sense to review the specialization as a whole and not just #4. So, the instructors are very cool and engaging, know what they are talking about and can explain the material reasonably well. It's not a very math heavy course, but it's not watered-down either. I would say it's just about right so one can gain intuition and understanding of what's going on and then go and read more complex books / papers. I also liked that there were "usually omitted" topics covered (like LSH, mixtures of gaussians, kd-trees pruning for nearest neighbors). I encourage you to read syllabuses before you start to get excited about what you will learn. One thing I didn't like was the amount of code written in exercises. I mean, I didn't have to write hardly any code at all, and when I had it was always very clear what I should do. If I were to start the specialization today I would've used the "sklearn way" (there are very detailed instructions for sklearn) and wouldn't have used graphlab at all. In any case, exercises are always very helpful in making sure you really understand what you've learnt, so I purchased the course #4 (I finished #1-3 before Coursera switched to the new pricing model, but I would have payed for #2 and 3 and would have skipped exercises and quizzes in #1 as it's very easy introductory course and if you plan on taking further courses you will be doing similar exercises anyway). Overall, 5/5 specialization, very helpful.
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Greg Hamel profile image
Greg Hamel profile image
8/10 starsCompleted
  • 116 reviews
  • 107 completed
3 years, 7 months ago
Machine Learning: Clustering & Retrieval is the fourth course in the University of Washington's 6-part machine learning specialization on Coursera. The 6-week course covers several popular techniques for gro... Machine Learning: Clustering & Retrieval is the fourth course in the University of Washington's 6-part machine learning specialization on Coursera. The 6-week course covers several popular techniques for grouping unlabeled data and retrieving items similar to items of interest. After a short intro in week 1, the course covers k-nearest neighbor search, k-means clustering, Gaussian mixture models, latent Dirichlet allocation and hierarchical clustering. It is recommended that you complete the first 3 courses in the specialization track before taking this course, but you could take it as a standalone course as long as you know a bit of Python and probability. Grading is based on a series of comprehension quizzes and labs, but you must pay for a verified certificate to gain access to graded assignments. Thankfully you can still download and complete the labs without doing the associated quizzes, so you won't miss too much as a freeware student. Clustering and Retrieval has a good balance of lecture content and labs that illustrate concepts covered in lecture. The professor is easy to understand and the lecture slides and are well done. The course generally has good pacing and devotes plenty of time to each of the main weekly topics, taking care to explain important considerations like different algorithmic approaches to each method and similarities between different techniques. It does, however, go off on a couple tangents, introducing map reduce and hidden Markov models, neither of which are covered in much detail or addressed in the labs. The labs use a data set of Wikipedia articles about famous people as an example to illustrate clustering and retrieval. Using the same data set for multiple labs is always a good idea because it lets students focus on the techniques themselves instead of having familiarizing themselves with new data. The amount of actual coding you have to do in the labs is minimal. The labs are more like interactive explorations of machine learning techniques with occasional one-line fill in the blanks than full-on coding assignments. You'll spend more time reading text, running provided code and analyzing results than writing code yourself. You can look at and answer the lab quiz questions as you go along but you can't actually submit them and get graded feedback without joining the verified track. Machine Learning: Clustering & Retrieval is a great course that covers the many most common clustering techniques with adequate depth while remaining accessible. Although the coding required is minimal, it is not an easy course: some of the concepts may take a couple watch-troughs to sink in and you may struggle with certain concepts if you don't have prior knowledge of probability. Aside from the need to pay to gain access to graded quizzes and few topics that felt tacked on, there's not much to dislike about this course. I give Machine Learning: Clustering & Retrieval 4.5 out of 5 stars: Great.
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