Nice course

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I can feel Prof. Howe tried to cover as much as possible and to build a foundation for both practicing as well as further study on the topics. However, I do feel it is not patient enough to give a detailed yet easy-to-follow explanation for some of the topics, and I had to do quite some self-readings to close the gap. I think it will be helpful if the course can provide some reading materials on how some of the formulas are derived (e.g. gradient descent, logistic regression etc.) as a supplement.

Meh, if you want to really dive in predictive analytics go to other courses.

rather nice course. learn R before joining

Same as before, subjects are quite interesting, but the video material is of quite low quality.

Excellent!!

Very good approach to each method; the assignments are a good test for the topics.

great for learner

I think the amount of course work to lectures was more appropriate than the first segment. I enjoyed the exercises and felt that they mixed the correct amount of theory and applicaiton.

Excellent Lectures. Since the course is several years old the organization of some of the assignments needs updating. That's the only reason I gave it 4 instead of 5 stars.

The lessons are sometimes completely disconected from the graded assignments. There were some graded assignements that dealt with things I have never heard about and I completed it without even looking the lessons videos. Some of the lessons are disapointing of the lack of assistance to the required software/code to be used. In such a way that the concept worked is very simple, but if you have no experience on the software or code you can have a hard time to complete the assignements with irritating details which are not explained at all in the lessons. The lessons serves more as a guide to what you should search in google and learn through other source of information. I did not expected such poor course from a paid one; I have doen free courses way better than this course. Don´t pay or this course, find some other course free or other paid course with better reviews.

The course is good. But it does not has lecture slides that is better for students to understand.

Great overview of many models and techniques, but very high level. Would have greatly benefited from links to resources to learn more about all the subjects. This course leaves students with only basic knowledge of the subject matter, which is fine considering the course timeline. But, for those who want to explore further please recommend sources of additional reading and research.

This course is quite outdated. I didn't learn much beyond what I already knew before I started. The Spark courses from edX are way better than these. Hopefully "Big Data Analysis with Scala and Spark" from the "École Polytechnique Fédérale de Lausanne" (also from Coursera) is good (I know their Scala courses, which are taught by Martin Odersky, are quite good).

There are very few quizzes between lectures and the assignments are not very challenging.

Many of the videos, specially the ones at the end were extremely rushed over. They serve more as a review if you know the subject, otherwise I don't think most people will get much from them.

The audio isn't very good for most of the lectures, many having an very annoying chirping sound (from when you leave an old flip phone near a computer... "teh-teh-teh teh-teh-teh teh-teh-teh teh-tehhhhhh....". Gosh, I haven't heard this sound in maybe over five years...).

The Kaggle competition at the end of the course can be fun if you do the hard work, but you don't need to put much of an effort to pass. I know that the submissions I peer reviewed were quite poor, but the grading criteria that we need to follow as reviewers is quite vague and not very thorough. You also run the risk of getting a lesser grade than you deserve because your reviewer is incompetent, which is a bummer... At the moment the course has very few people taking it (the same people I peer reviewed, also reviewed me, which leads to me to believe that maybe only 3 or 4 people were taking this course during the November 2016 iteration).

Fantastic course! Excellent conceptual teaching for people who already know the subject but need some more clarity on how to approach statistical tests and machine learning.

I felt that topics were not treated in enough depth. It was a lot of topics to cover in a 4-week course.

I learned a lot from the kaggle competition.

it gets on my nerve from 3rd Work onwards

This course covers a lot of material, but unfortunately lacks depth and thorough examples in many areas. It could also use more hands-on activities. Overall, I learned quite a bit and found it was worth the time and effort.

The entire course is an overview! This course will be a revision if you already know the concepts.

Nive that the course covered a broad range of topics.

And good to get pushed to do some kaggle competition and peer review.

Content is too easy

I'll say the same about this class as the rest of the specialization, if you have the skills to complete this course then you don't need to take this course. If you don't have the skills to complete this course, you will not complete this course. The course instruction is at 10000 feet level and the assignments are very challenging and the course will NOT teach you the skills required to complete the assignments.

I recommend the Machine Learning Course (from Bill's colleagues) at University of Washington. That is a course where you get some real instruction and understanding of how to complete assignments (though still very challenging).

Excellent thoughts and concepts presented.

A quick overview of technology terms used for Machine Learning, and gentle introduction into learning through Kaggle.