I noticed some (not critical) mistakes here and there during the video lectures and the quizzes.

Over all, a good course, but I think in order to gain a full understanding of the material one needs to look deeper into the literature. The course provides a good starting point.

The course explains all the basics of Machine Learning in very simple and easy language; even the statistical and advance mathematic concepts are explained simply and graphically & also only as much in depth as needed to understand the ML algorithm properly.

Thanks!

Working with the IBM notebook for the final assessment was a bit of a pain

And the peer assessment has no recourse, someone marked me down for something i had, i guess they didnt notice? but i dont have an easy way of getting that changed

Content is good stuff though

Very Good Crash Course. Concepts are explained efficiently without wasting much time on the Mathematical/Statistical stuff. Awesome Notebooks are given to code along with the concepts which is really helpful. Must learn deeper and further to become a Professional.

It is a bassic initiation on machine learning, it has the bases of all the primordial algorithms. Its good to see and easy to understand. If i had to say one bad thing is that some labs has wrong things and you have to modified it. But it is a good course at all.

Content was good, but mainly classification. I was missing other aspects (like regression, deep-learning, ...). What really annoyed me a lot was the constant advertisement of the IBM infrastructure; for this course the IBM Watson thingy is largely overblown imho.

Reason for giving 4 rating

1) Proper explaination of all models & its concept

2) Exercise is good ,but you need to understand the python properly

Reason for deducting 1 rating-

1) No explaination about preprocessing of dataset

2) No pdf is given for future reference

Covers the basics and explains clearly the difference between regression and categorization. Lab work was instructive. Some of the material is now out of date, contains grammatical and spelling errors, and has inconsistencies with instructions and testing.

The hands on lab are not worth it, there is very less explanation about why we are doing particular step, take for eg final peer graded we just put Boolean value based on just the attribute value of master or above. Very poor last project to be honest.

I liked the course overall. Some of the lectures did not break down real world data sets or examples as much as I would have liked. Additionally, it would be nice to have more real world data set examples or tutorials to study or analyze with Python.

The labs are great and the videos are spot on. However, there are numerous typos here and there and also the final project grading rubric had some issues and did not provide some people with guidance and submissions that were correct were marked wrong,

The course information was good. I wish the videos would have touched on the pythonic code for these examples, even just a little bit. The bad was that the labs were down through the majority of my class which made it difficult to see the python part.

I recently completed the "Machine Learning with Python" course, and I must say it has been an incredible journey. The course content, structure, and the hands-on approach make it a standout choice for anyone diving into the world of machine learning.

All classes was very well designed and structured. In my opinion was the best course I done by coursera. The inconvenience was due to IBM Watson. The Lite service plan offers 50 free hours of free use and I received 10 as the time limit.

Thank you.

A very good course to grasp the foundamentals of Machine Learning using python. Besides the math explanations, i reccomend to have at least a basic knowledge of python, in order to explore the jupyter labs which, in my opinion, are solid examples.

Really interesting course. I would have enjoyed if it went into more depth in some of the topics, for example being more specific with certain algorithms. Would also have liked a peer graded assessment on multiple topics (not just classifiers).

Excellent overview of machine learning in Python. Only reason I didn't give it a 5 is because I would have liked a little more content introducing more of the sklearn library in order to be better prepared for working on the project correctly.

Hi all.I am so glad to participate this course this course provide me the practical exposure of the machine learning.And Add a credit to my resume and increase the ability to build a "ML model". Great and earn a certificate.from IBM is worthy

This was the meat of the IBM data science course set for me, and was really very informative. Extremely well presented and clear. I would have liked a bit more depth in this material, with a bit less emphasis on python/sql/tools issues.

The Course is very valuable content for beginners and easy to understand, the explanation is very good with simple words and live examples. i had refereed this course to my friends to improve their technology stack, their feeds is also good.

More interactive questions for the programming exercises. Also, the peer marking has room for error since we can't always identify mistakes in others code easily. A code that checks answers for each point and gives feedback would be better.

Good introductory course for people to start off with Python. This course touches upon various aspect of the coding language and the lab environment made it easy to practice things. Looking forward to such informative courses going forwards

The theory is very well explained with nice examples. However the course doesn't explain very well how to use python. You can only open the Jupiter notebooks without providing explanations for the various steps and orders that are used.

4 stars = Great Course!

missing start = Cloud Management / course interrupted because of month credit expired, expected promo code never available...

Suggestion : Cloud "Assets" available for the entire course without a stupid limitation

All over about the course is good but little bit math behind the algorithm was not explained (iterative) and implementation with python also not discussed. Over all you will get to learn many things from this course.

Thanks

All d best