Course is good but leaves a lot of things unexplained and feels like the weeks explaining ml algorithms are in a rush.But the assignments are truly remarkable.I would recommend this course to anyone who already knows machine learning and would want to apply it on some good problems/assignments before Kaggle.

I really like the main idea of this course, i.e., using sklearn lib along with basic lectures on the ML topic. So, I was expecting that we will be following the contents of text book by A.C. Müller & S. Guido. In the first two weeks it was really good. The materials of last two weeks were quite compressed.

Its a really complex topic an though videos seem long enough to explain some ascpetcs of it, many little things go under the radar and make it difficult to understand some thing. Algo, the lectures are a bit weird since the professor sometimes stutter or changes ideas mid sentence.

Good explanations on videos, The only problem which was really time consuming and wasting was the problems related with the assignments submission. but overall this course helped me a lot to structure machine learning fundamentals in my mind and to get a good practice out of it.

The videos are way too long and very monotonous. They should be cut down and reduced. The maximum length they should be is 5-6 mins other wise they becoming distracting.

The course content is good though. Quite easy to understand but going through the videos is a chore.

I think that the subjects are very advanced. There should be a more clear specifications of prerequisites for the course. I had to look for lot of help outside the materials provided for doing the activities. The course is fine if you have the apropiate skils though.

It's a good course, but a quick one. One needs to have a beforehand knowledge of all the algorithms as they are not discussed in details. State of the art is not mentioned. Implementation and best practices are present, along with pros and cons of each algorithm

The course is quite high-level. There is nothing wrong with an applied course being high-level. The material is easy to follow, the quiz is a bit challenging but the homework assignments are quite easy to pass. I prefer a course with more fundamental details.

The course is too tight, just 4 weeks cannot master the machine learning. This course can splitted into 2, in order to capture more on the deep learning and unsupervised learning, which are important, but being categorized as option in the course.

Good content but too fast paced for someone without even the slightest basics on ML. The first 2 courses in the specialization did not prepare for this course. To make full use of the course get ML basics right and then maybe come here

Full with a lot of important information, however the delivery is very weak. With all respect, this was one of the most boring courses I have ever had. Seeing the unmotivated instructor makes the course very difficult to follow.

the second assignment was a little beyond what was taught in the lecture. others are fine.

big suggestion: please please have a better auto-grader. Most of my time was spending on how to battle the auto-grader instead of coding...

The course is basic, and they skip too many details; often, it seems like they only created the course for the sake of it. I think they try to cover too many topics in a single course. The Python codes are also quite average.

there is a little suggestion from me, the correction system is a bit strange when I did the test when I ran it it worked, but after submitting 0/100. But I kept trying to submit, suddenly it corrected itself and had a value.

What is taught in the lessons and what is asked in the assignments have nothing to do with it. Assignments are too difficult. You can try if you want to challenge, but if you're just learning, I don't recommend it.

Course is well structured, course material also is well defined and learning is excellent. Though Instructor's communication is very laidback. Should have more engagement in tone and connect with enthusiasm.

Quick tutorial-like overview. Autograder is not too verbose and as a result I spent some time struggling with debugging the code rather than figuring out how to solve machine learning related problems.

Good content and I learned a lot. However, the instructor made too many mistakes during the lectures and the assignments also have mistakes that need to be fixed by the students.

Follow the course with introduction to ML with python to have descent understanding. Instructor won't be able to keep one interested for long. Exercises could have been tougher.

Two point: 1) you can learn a lot here, 2) imagine you are shown a hammer but never explained how to hit a nail. Two previous courses in the specialization do both.

Course instrutor and materials are needed to be improved as they are very poor

Assigments\Quizes are very good and they are the mainly root cause for this rating

Some very good practical advice like dummy testing or data leakage issues Some trivialities and repetitions. Python code could have been a bit better commented

there should be some low level usage of sentences for a intermediate programmers,most of times it bounces up the mind ,not able to get the required concept

Know some existing machine learning functions and packages from sklearn, but really don't know how to improve prediction accuracy within each function.