First, coursera is a ghost town. There is no activity on the forum. Real responses stopped a year ago. Most of the activity is from 3 years ago. This course is dead.

Two, this course seems to approach the topic as teaching inadequate ways to perform various tasks to show the inadequacies. You can learn from that; we will make mistakes or use approaches that are less than ideal. But, that should be a quick "don't do this," while moving on to better approaches

Three, the professors seem to dismiss batch learning as a "dodgy" technique. If Hinton, Bengio, and other intellectual leaders of the field recommend it as the preferred technique, then it probably is.

Four, the professors emphasize log likelihood. Mathematically, minus the log likelihood is the same as cross-entropy cost. The latter is more robust and applicable to nearly every classification problem (except decision trees), and so is a more versatile formulation. As neither actually plays any roll in the training algorithm except as guidance for the gradient and epsilon formulas and as a diagnostic, the more versatile and robust approach should be preferred.

The professors seem very focused on decision trees. Despite the "apparent" intuitive appeal and computational tractability, the technique seems to be eclipsed by other methods. Worth teaching and occasionally using to be sure, but not for 3/4 of the course.

There are many mechanical problems that remain in the material. At least 6 errors in formulas or instructions remain. Most can be searched for on the forum to find some resolution, through a lot of noise. Since the last corrections were made 3 years ago, the UW or Coursera's lack of interest shows.

It was a bit unnecessary to use a huge dataset that resulted in a training matrix or over 10 billion cells. Sure, if you wanted to focus on methods for scaling--very valuable indeed--go for it. But, this lead to unnecessary long training times and data issues that were, at best, orthogonal to the overall purpose of highlighting classification techniques and encouraging good insights about how classification techniques work.

The best thing about the course was the willingness to allow various technologies to be used. The developers went to some lengths to make this possible. It was far more work to stray outside the velvet ropes of the Jupiter notebooks, but it was very rewarding.

Finally, the quizzes were dependent on numerical point answers that could often be matched only by using the same exact technology and somewhat sloppy approaches (no lowercase for word sentiment analysis, etc.). It does take some cleverness to think of questions that lead to the right answer if the concepts are implemented properly. It doesn't count when the answers rely precisely on anomalies.

I learned a lot, but only because I wrote my own code and was able to think more clearly about it, but that was somewhat of a side effect.

All in all, a disappointing somewhat out of date class.

Relying on a non-open source library for all of the code examples vitiates the value of this course. It should use Pandas and sklearn.

Very impressive course, I would recommend taking course 1 and 2 in this specialization first since they skip over some things in this course that they have explained thoroughly in those courses

Hats off to the team who put the course together! Prof Guestrin is a great teacher. The course gave me in-depth knowledge regarding classification and the math and intuition behind it. It was fun!

It will definitely help you in understanding the basics to dept of most of the algorithms. Even though you are already aware of most of the things covered elsewhere related to Classification, this course will add up up a considerable amount of extra inputs which will help to understand and explore more things in Machine learning.

Very helpful. Many ThanksSome suggestions:1.Please add LDA into the module.2.It is really important if you guys can provide more examples for pandas and scikit-learn users in programming assignments like you do in regression module.

Quite Interesting. Entertaining and the lectures are quite easy to follow.

Very good lessons on classification.

It's such a well organized course. Concepts are taught in an interesting way and made simple to understand through examples that thread along the course. I would recommend any aspiring data scientists to take this course. Thank you Carlos and Emily.

Excellent lessons on this important topic Classification. I think all major areas were explained quite nicely, with proper examples.

This course covered very interesting aspects of real-world applications for machine learning. From my point of view, the theory was very clear an valuable, until that point that the programming assignments closed the cycle beautifully.

Good Course!!

the person who wants to start career in machine learning must take this course! Its awsome :)

Very good course for classification in machine learning - top presentation documents - very well structured and practical

one of the best experience about this course i gained I learned a lot about machine learning classification further machine learning regression thanks a lot Coursera :)

Great course, provided details that not show in others' and textbooks.

Nice!!

Brilliant course!

good

It is very intuitive and easy to follow.

I hope you add SVM and talk about linear/nonlinear decision boundaries in the next enhancement to the course.

This is the best course ever that can happen in ML...I did not know anything, but after taking this specialization, my understanding of ML has dramatically improved

Would recommend without any reservation - Prof Gustrin and Prof Fox are the best!!!

Very focused overview of different classification methods. Goes deeper than in other ML classes.

Awesome!

great course, would have been even more great if there are more details on how to use boosting for kaggle

Great Course