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

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.

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

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

I was not satisfied with the way my final assignment was graded wrongly without any feedback. But overall the course is definitely helpful in introducing machine learning concepts with implementation using popular python libraries

The content in the course is very detail and clear. They illustrate each time difference technique where we can use in machine learning. Though, I found the exercise in are a little bit easy, but it's help a lot with learning.

Best course to understand basic working of all algorithms. Assesments are goof for fresher and looks easy if one knows already. Ensemble techniques should also be included in this such as RandomForest and Boosting Algorithms.

Good content, but a few technical issues with Lab.

Possible improvement can be having lectures also about coding ML models in Jupyter, rather than just having theory lessons.

The provided notebooks are well written and clear

Great Course to get started with the practical Machine Learning, This course is for beginners who wants to get to know the Machine Learning Concepts and its implementation.

Great Step for the next courses like deep learning

This is a good course for catching up with fundamentals. Although most of the techniques and algorithms discussed here are not widely used nowadays, they are still good to know and useful for simple and small datasets.

Very informative, I learnt a lot about model training and machine learning techniques. However I found some parts of the materials were jumping too fast to result i.e. not enough step to step explanation for the codes.

One of the better courses in the series. Lab sections can be better with more practice questions. Final project could have been more comprehensive as well instead of focusing on just one section in the entire course.

A good course to start your AI journey with python and scikit learn. Four stars because code should be explained in a video, but it has an advantage that when you search something you always discover something new.

This course provides excellent practical implemented datasets which gets you started but a person willing to do this course must have to learn various things on his own as well to completely understand this course.

Videos contain great content, are very clear and to the point. However, the malfunctioning Lab environments really took the speed (and fun) out of the course. Overall it was an interesting and valuable course.

if only financial aid was available for this course it would have been awesome, the content is pretty good, but the labs are pretty confusing as I haven't been able to figure how to register them as completed.

Es un curso muy completo que cubre muy bien los fundamentos básicos sobre machine Learning. Al final de este curso tendrás una noción de que algoritmos son útiles para cada una de las necesidades mas comunes.