Learner Reviews & Feedback for Machine Learning: Classification by University of Washington

Another great course from this specialization. Tremendous effort in making the notebooks and assignments. I just think there could be recommended readings also.

Excellent Course, I'm very found of Carlos jokes mixed with the hard concepts ^^. Lectures are precise, concise and comprehensive. I really enjoyed diving in depths of the algorithms' mechanics (like Emily did in the Regression Course). I also deeply appreciated the real-world examples in the lectures and real world datasets of assignments.

Some may regret the absence of a few "classic" algorithms like SVM but Carlos definitely made his point about it in the forum and did not exclude the addition of an optional module about it.

I found some of the assignments less challenging than during the Regression Course, but maybe I'm just getting better at Machine-Learning and Python ^^.

Thanks again to Emily and Carlos for the brilliant work at this very promising specialization.

Really liked the practical application of this course - very useful in learning classification methods.

Well this series just doesn't seize to amaze me! Another great course after the introductory and regression course. Though I really missed detailed explanations of Random Forest and other Ensemble methods. Also, SVM was not discussed, but there were many other topics which all other courses and books easily skips. The programming assignments were fine, more focused on teaching the algorithms than trapping someone in the coding part. This series is the series for someone who really wants to get a hold of what machine learning really is. One thing which I really like about this course is that there are optional videos from time to time, where they discuss the mathematical aspects of the algorithms that they teach. Which really quenches my thirst for mathematical rigour. Definitely continuing this specialisation forward

Great course , It gives the idea of how we should do classification from scratch as well as understanding the concept of how to handle large dataset during training. Boosting is one of the most important technique as what I have heard in machine learning and it's great to understand the concept of it.

The best course I could find to get a strong hold of the basics of machine learning. Presented in very easy to follow steps with thorough coverage of all the concepts necessary to understand the big picture of each algorithm.

So cool and much practical.

Great course and learning

Curso excelente, desde o material, as atividades práticas e aulas. O fórum de discussões é repleto de pessoas interessadas em ajudar. Essa é a especialização a longa distância definitiva de Machine Learning.

Best ML course I've ever taken!

This continues UWash's outstanding Machine Learning series of classes, and is equally as impressive, if not moreso, then the Regression class it follows. I'm super-excited for the next class!

Excellent course except that week 7 th assignment based on ipynb notebook had some redundant questions. Otherwise a good course especially sheds light on Adaboost, ensemble classifiers and stochastic gradient with batch processing.

Thanks Professor Carlos.

Excellent course. Great mix of theory overview coupled with practical examples to work through.

I found carols to be the best instructor in machine learning domain, he presented the algorithms and all core machine learning concepts in really great way.

Great course with very good material! I'd like to see assignments that leaves more coding tasks to the student.

Well explaned !

Very nice course with a little bit of details about how classification is done. Enjoyed it.

great

Wonderful experience. Prof is very good.

Really interesting course, as usual.

This is a hand-on very exciting course, strongly recommended for all audience

The course is demanding but I learn a lot in classification.

The teachers are awesome!

The best part for me in this specialization, Classification is great

This is really a wonderfull course, and i recommend it to anyone who want to master some important techniques in the trending field of machine learning

The course walk through (and work through) concepts of linear classifier, logistic regression, decision trees, boosting, etc. For me it is a good introduction to these fundamental ideas with depth but not too deep to be distracted.

I personally become interested in knowing a bit more theoretical basis of the tools or concepts like boosting or maximum likelihood. The course understandably doesn't go that much into math and theory which leaves me a bit unsatisfied :P. But that is probably too much to ask for a short course and I do think the course covers great materials already.