very understandable

Completed this course and found it to be highly educational and well-structured. The lectures clearly explained complex concepts, and the course provided a solid foundation in both regression and classification techniques. However, the coding portion could be more engaging. Integrating more code demonstrations and visual discussions within the lectures would enhance the learning experience. Overall, it was a great course, and I highly recommend it!

Very clear and intuitive explanation with a great instructor, though the contents are a little too easy, especially for people with a STEM background. More exercise could be set with less guidance (currently it's like writing ten lines of codes for every week of learning). Also, it would be nice if there could be an exercise dedicated to the use of packages like scikit-learn in depth, since that is what most people will end up using the most.

A very good introduction to Machine Learning. I would prefer some more math since this gives me more confidence in understanding, but the course is aimed to a wide audience so that's acceptable. I accompanied the course with Andrew Ng's notes on machine learning.

Andrew sir's teaching made the course interesting and exciting. However, the course was too easy and some more mathematically oriented discussions could have been done.

A good intro to ML. Strikes a good balance of the theoretical and practical aspects of Supervised ML.

If some small project type of stuffs are added in the course, that would be more of a help.

very good course

From the enthusiastic ratings given to this course, I had thought it was really math centered and hard to attend. Honestly the course is all but hard if you have a very basic knowledge of python and calculus. 20 years after having attended my calculus 101 lectures, I'm still able to follow very easily mr Andrew. I hope the course gets harder and more challenging with the sections 2 and 3 or I'm afraid I'll have wasted my money.

The course dives too deep into the math behind the type of regressions to make predictions. But it lacks practice in using python libraries to actually put them into use. Instead of having to calculate each operation manually it should present the learner with more opportunities to implement python machine learning libraries to get some hands on experience.

Could give a bit more focus on the coding part. What I mean is rather than giving notebooks with already written code, you could teach the code to the students and have them write it themselves.

iIt was my frist machine learning course , learned many concepts and this course created more interest in learning advanced algorithms and explore much more concepts

It is a good course for complete beginners, but for those who want to know things in detail, this course just doesn't quite cut it. It skips the details too much.

Couldn't practice a lot (just using formula learnt). Need more project orientated coding .

I passed the "Machine Learning" course by Andrew NG in 2019, and I took this course as a part of my specialization. So this course looks like a simplified and cut version of its predecessor. Most of the Lab files are not downloadable as PDFs, so, after the course finishes - you can't have that data with you. Quizzes are so much oversimplified, like 2 questions, I think in the previous course it was much harder and it took me time to learn things. This review is a bit chaotic, I am sorry for that. I know that Andrew Ng is a great teacher, and in the previous course, he gave so much more valuable information and the simplification of that course makes me sad.

I found it hard to follow and confusing. By the time, we finally got to do some hands on work, I felt totally unprepared. Could have done more to explain the python code or to work the student slowly into the coding assignment. All in all, I can not say if it a good specialization, yet after the first course I am looking for something more hands on and beginner friendly. I am a bit disappointed as I have heard only stellar things about this course.

The quizzes and labs are too easy to be of value, with many of the quiz answers literally being written in the image displayed above the question, and labs basically just require you to translate a specific equation into Python.

There isn't really a way to tell if I understand the content or not, I recommend you do not pay for this course.

I cant seem to access my optional lab materials even when i have not exceeded deadlines and dont have have an outstanding payment. it keeps telling me session timed out and keep repeating the same when i click the reopen button. i really enjoyed the course but i didnt like the fact that i couldnt access my optional lab material.

Very, very very basic. If you're looking to freshen up some concepts, this is not for you. I wouldn't even recommend this course to CS students as the course avoids at all costs at explaining the maths behind machine learning.

I was expecting something more challenging and more in depth

Even after paying the full amount for the course upfront, coursera locks optional labs and assignments if the deadline is not met. It asks me to buy the whole specialization series again! My god! Online courses should be flexible w.r.t. timelines. Whenever I find time, I use coursera to learn new things. But this limited time content availability beats the whole purpose. Very very very disappointed.

Absolutely not worthen this money. This course is suitable only for non-technical people without any experience in ML/DS.

no refund on 5.24 when last payment is 5.20

shit-ass course.

unenrolled