Bewertung und Feedback des Lernenden für Applied Machine Learning in Python von University of Michigan

lectures are boring, not updated but yes i learned something, but its not up to the margin

I had high hopes going into this course after the really well put together courses 1 and 2 in the specialisation, however the video material was dull and disengaging. Where the lecturer could have spend hours going into the ins and outs of how the different algorithms work, instead the course followed a structure of: 1 - Brief overview of an algorithm, 2 - whats the syntax in scikit-learn, 3 - what parameters does it take, 4 - what other commands are there

I was really disappointed, as most of the actual learning was done from reading other sources on the web and watching videos for free on YouTube. I guess the only positive is that because I paid for it I was forced to finish it?

worst course of this specialization so far , the instructor is just reading stuff not making any effort whatsoever and it seems like he's obliged to do teach this course ,the autograder is the worst and the journey with this course is really painful i hope that you take these points in consideration and just delete this course

The materials of this course is poorly arranged: how is that even possible to cover gradient boosting, random forest, neural network, and unsupervise learning in a single week?

This is a really bad quality course. A little bit more professionalism would be advisable. I will continue to the next course and leave this behind.

Faced problem in every assignment while reading the data sets. If the data is not in that folder what is the point of telling so?

1- very slow paced lectures

2- very basic and elementary examples

To sum up, it is boring and not useful for practical application.

I am not happy with the course material and the way teachers are teaching.

This is not a good course for beginners.

no teacher intraction

Great course for the applications of machine learning. While I wouldn't recommend for someone with no ML experience, this was a great course for an R user trying to learn more python!

Very helpful and well-structured course, clear lecturing, and high-level assignments. I hope, however, if it can be offered another course specialized in unsupervised learning in ML.

Great Course. I love the way it is designed, delivered. I learned a lot. The most important part is that I enjoy every bit of the session and completed everything less than a week,

Course was designed in a well structured manner and the basic concepts were covered for Regression and Classification. Many many thanks to University of Michigan for creating it.

The course is awesome. Professor Kevyn Collins Thompson, explains the topics with examples in python which makes content easy to understand. It is the best course for beginners.

It is very good blend of study and practical assignment. Assignments were very well designed to greatly enhance the understanding about the things learned in the video lectures.

This course was challenging and extremely interesting. The long and detailed lectures and excellent lecture notes covered the material very thoroughly for an online course.

ML is a wonderful course.I learn new concepts with hands on experience.Each and every algorithm concept is clearly explained .I learn how to handle real time data set.

I​t's a really good course. I teaches you the basics of many ML algorithms. I really recommend it for begginers

This course is an survey on how to implement many machine learning techniques using the SciKit Learn library. Following the course, you can learn several interesting details about how to work in the field, but it is important to take into account that it is not possible to learn the algorithms during the course, since a huge amount of material is covered during a short time; to make the most of the course you have to know them in advance. It bothered me to discover that the course was planned for five weeks but Coursera has reduced it to four, removing the possibility of practicing exercises on unsupervised learning.

Overall a good course; I learned a lot. But hard going at times for someone new to Python and Jupiter Notebooks. The time estimates for the module assessments are way under (maybe reasonable if you are already a Python expert and have some familiarity with the relevant libraries, but that's not my situation). File location mismatch between Assignment notebooks environment and submission / assessment environment was very frustrating.

Since there are many theoretical concepts in this course, like model evaluation and tuning parameters, it would be much better if those are explained using real or semi-real life problem examples. Especially the quizzes needed more context as to why a particular situatrion might occur, and why that particular variable of interest is necessary.

어려웠어요 ㅠㅠ

This is an incredibly dry course from the University of Michigan. In typical academic fashion, it churns out a bunch of lectures, expects you to remember the content, then throws you straight into some quite complicated problems. Half the time, these problems don't even work and you have to dive into the forums to find out how to correct mistakes that the content providers have failed to correct themselves, even several years down the line. There are iPython notebooks you can use to follow along with the lectures, but really they could do with useful information and explanation embedded within them, which is one of the main strengths of iPython notebooks and has been sorely underutilised here. If the course material were presented in a more interactive and engaging manner, the learner might be more motivated and engaged when solving assignment problems. As it is, unless you have prior knowledge or experience within the field, or a mountain load of free time, it's more an education in frustration than machine learning.

The quality of this course in the series is a far cry from that of module 1 and 2, which is a shame because this is the one that I was really looking forward to. The professor does not seem comfortable and uses a lot of extra words in his lectures which can make them confusing and rambling. Many questions on the quizzes and assignments are not covered or well explained by the material. Many assignment questions have to be explained by teaching staff on the forums because the task is not clear.