Bewertung und Feedback des Lernenden für Maschinelles Lernen mit Python von IBM

I liked the course but felt that a beginner would not be able to cope up with the speed. However, if someone already has a basic knowledge of data analysis using python, this course would be perfect. I especially liked how each algorithm was explained in detail, how it works and what parameters effects them.

The courses prior to this course in IBM Data Science for Professional were simpler, and the codes were easier to understand. Some of the codes in the labs session for this course were difficult to understand. However, overall the course was a lot more effective and more in depth.

Thank you for this service.

Creo que este curso es muy avanzado para mi. Me gustó porque el instructor explicó muy bien de donde viene y como funciona las distintas metodologias, pero a nivel de practica algunas cosas no fueron explicadas y en el projecto final fue un poco confuso iniciar aunque realmente era aplicar las practicas.

Material was great. Tutorial videos were great. The only improvement I would expect is on the Labs. Not only the lab environment was slow but there were certain errors in the questionnaire for final assignment for quite some time which no one seems to correct. If this is rectified, its a great course.

It's a good course to have some idea about ML case. A good part of the course is that, it has some brief expenations people to let them work further. Maybe not a learning course but a very good start. I do appreciate the instructors capacity to give lecture and summirize. Thank's for all the afford!

Loved the content, there needs to be more explanation about what Python code is necessary to complete the final project. In many cases I had to convert strings to integers and use plotting codes outside of examples. Please provide references to use that are necessary to complete the final project.

Great course for machine learning beginers. Helped me gain some general ideas about machine learning, and I'm able to conduct simple machine learning algorithm on my own after the class. Would be better if the python code has more detailed explaination, or has videos walking us through the code.

The course thoroughly teaches about all the mathematical formulas and theoretical explanation in creating and predicting of data in data models. But I think if he also teaches about various python modules used for data science too, then it would have been much easier to do the assignments.

The content is good and broad, although a little too superficial sometimes. It also provides a fair Python practice. My biggest complaint are the quizzes, that are full of bugs. Those bugs are the number one complaint in the forum, but there is very little responde from the management.

This is a really good course, where the instructor is clear, detailed when needed and practical in his examples. The only downsight is that this, as every other course of IBM Data Science program, is not designed to be a part of a full course: Often it repeats some previous concepts.

The course is very useful for the beginner as me. It contains a good math explanation of ML methods. The technical Python part in labs is good enough too for the beginner in Python/ Sometimes it don't correspond fully to the math part but it isn't critical. Thanks for the course!

Good course syllabus. Some improvements needed: Jupyter notebooks contain many English typo errors. The final assignment rubric uses the wrong normalization technique for the test data where test data were normalized by itself instead of training a scaler with the training data.

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.

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

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.

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,

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.