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Applied Machine Learning in Python に戻る

ミシガン大学(University of Michigan) による Applied Machine Learning in Python の受講者のレビューおよびフィードバック

4.6
4,378件の評価
758件のレビュー

コースについて

This course will introduce the learner to applied machine learning, focusing more on the techniques and methods than on the statistics behind these methods. The course will start with a discussion of how machine learning is different than descriptive statistics, and introduce the scikit learn toolkit through a tutorial. The issue of dimensionality of data will be discussed, and the task of clustering data, as well as evaluating those clusters, will be tackled. Supervised approaches for creating predictive models will be described, and learners will be able to apply the scikit learn predictive modelling methods while understanding process issues related to data generalizability (e.g. cross validation, overfitting). The course will end with a look at more advanced techniques, such as building ensembles, and practical limitations of predictive models. By the end of this course, students will be able to identify the difference between a supervised (classification) and unsupervised (clustering) technique, identify which technique they need to apply for a particular dataset and need, engineer features to meet that need, and write python code to carry out an analysis. This course should be taken after Introduction to Data Science in Python and Applied Plotting, Charting & Data Representation in Python and before Applied Text Mining in Python and Applied Social Analysis in Python....

人気のレビュー

FL

Oct 14, 2017

Very well structured course, and very interesting too! Has made me want to pursue a career in machine learning. I originally just wanted to learn to program, without true goal, now I have one thanks!!

OA

Sep 09, 2017

This course is ideally designed for understanding, which tools you can use to do machine learning tasks in python. However, for deep understanding ML algorithms you should take more math based courses

フィルター:

Applied Machine Learning in Python: 701 - 725 / 740 レビュー

by Ankur P

Mar 30, 2019

Unsupervised learning was missing. The codes written in the lectures were not explained clearly. Some topics looked unimportant.

by Andy S

Jun 04, 2019

It could have been better with more examples.

by Nigel S

Jun 10, 2019

This is an OK introduction to Machine Learning. It covers a range of relevant topics. The gap between the lecture content and the assignments is the typical chasm for this U.Michigan "speciality", and frankly you end up basing assignment answers more on internet research rather than lecture content.

I'd sum it up as a substantial missed opportunity. The last assignment is really good in terms of doing a realistic Machine Learning project, but the preceding course content doesn't give you the tools or frameworks to do that project in a logical, industry standard workflow. It gives you an idea of what the tools are, but not how to really apply them all together in an efficient and logical series of steps.

It's as if those who designed the course decided that learners needed a tough-love approach, like a trainer lying down on the grass and showing learners swimming strokes, and then just throwing those learners into a pool and expecting them to keep afloat, and combine what they remember with what they see other more experienced swimmers in the pool doing. It shows a fundamental misundestanding of the Coursera learners usually being very time poor and expecting much more from the instructors.

by Ryan D

Jul 15, 2019

I'm glad there was an introductory course like this offered for machine learning. The content is very accessible and the assignments are simple enough to work through without frustration, but challenging enough to help you understand how to apply machine learning algorithms on your own.

I did purchase the book recommended, Introduction to Machine Learning with Python by Andreas C. Muller and Sarah Guido. The lectures in this course are basically paraphrase the book. Frankly, I think you'd get more value from this course if you read Chapter 2 in its entirety and follow along with the juypter notebooks provide with the book. It's easy to tell when someone is teaching you vs. reading to you— this course's lectures were definitely the latter.

by Claire Z

Jul 20, 2019

The course is quite high-level. There is nothing wrong with an applied course being high-level. The material is easy to follow, the quiz is a bit challenging but the homework assignments are quite easy to pass. I prefer a course with more fundamental details.

by Matteo B

Aug 10, 2019

Assignments are not really supported by the material provided (videos). The level is not balanced. Some bugs in the assignment code as well

by Halil K

Sep 27, 2019

Good content, bad teachng staff. Though the discussion forum contributors were very helpful and should be commended for their efforts.

by Jun L

Nov 07, 2019

There are too many errors in the video and even in the quizzes and assignments which will affect the final grade and wastes studying time to figure out it is an error. It is pointed out in the discussion forums but no one is taking the action to correct it. Moreover, at least 3 of the reading materials fail to be loaded.

by Mauricio A E G M

Nov 17, 2019

This course is not useful to learn from scratch, but has some good things, for example the final assignment.

by vikram m

Aug 26, 2019

It's a good course, but a quick one. One needs to have a beforehand knowledge of all the algorithms as they are not discussed in details. State of the art is not mentioned. Implementation and best practices are present, along with pros and cons of each algorithm

by Mario P

Dec 08, 2019

I struggled with this course. The lectures cover a great deal of information extremely fast. I appreciate that there are more lectures than in previous courses in the specialization and the information is better presented IMHO. The assignments were quite difficult and I struggled. Relying heavily on discussion forums and online posts.

by Gilad A

Jun 27, 2017

The last assignment was super. apart for it, the assignments and the course were too easy

by Dimos G

Sep 03, 2019

This course was a complete disappointment. First of all, it should have been split into two courses. The second week especially contains so much material to the point that it's not-pedagogical. Also, I regret to say that the instructor is not fit for this task. It would be better if they used Christopher Brooks from the first two courses as he is more engaging and he seems to have a lot more experience in public talking. Another thing is that there are serious bugs with the assignments. This course needs serious redesign.

All in all, don't spend your precious time and money on this one. There are better courses available on this subject.

by Adithyan U

Jul 03, 2019

The course tries to do too much in four weeks. Consequently, the teaching material isn't as comprehensive as it ought to be. I've probably spent over 10-15 hours cumulatively on other websites, trying to comprehend the intuition behind the algorithms used. This course isn't great at getting that across. There's a lot in here that we're forced to take for granted. I'm afraid I'll have to think twice before I choose other UMich courses in the future.

by Muhammad H R

Jan 19, 2018

This course was too theoretical and lacked any practical exercises that would help me solve any problems. The professor went too deep into the concept and in the end you were left wondering what is the purpose of the algorithm. Seems as if they were concerned in covering a specific amount of topics rather than making the concept of machine learning more approachable.

by Frank A N

Nov 20, 2018

It was too easy

by Olubisi A

Jan 11, 2019

I think this course would be a bit challenging to someone who is new to machine learning. The professor often glosses over import details and moves a bit quickly through the course material. There needs to be more powerpoint and reading material explain what the videos explain.

by Mahmoud

Dec 28, 2018

Week three is the worst ..

Lecturer is getting confused a lot in an already confusing topic which ofc makes me resort to outside readings in order to grasp it and leading to stretching the time I need to finish this week

by Justin F

Sep 27, 2017

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.

by Josh J

Jul 09, 2018

Although the course taught me a lot on the importance of parameter tuning and data leakage, I found that often times it was too technical and did not provide the information I was looking for. I found myself continuously referring to notes from other ML courses during the length of this course. In addition, the video errors and challenges with the auto grader were very frustrating.

by Dror L

Nov 25, 2017

great topic, poorly presented. material not well divided among weeks. lots of repetitions. lack of hands on practice until the very last task.

by Thomas M S

Feb 09, 2018

I do not have the impression after this course that I have reached a level of familiarity that I will continue using the content of this course. Disappointing.

by Gregory O

Sep 25, 2017

I was excited going into this course because the others in the series were taught well and I had learned a lot. Unfortunately, this course greatly disappointed. The lectures were dull, included a lot of mistakes, and did not cover most of what was expected during the assignments. All in all, this course was a waste of time versus just learning scikit-learn on your own.

by Milos P

Jun 27, 2018

Decent material and I appreciate the amount of hard work that went into building the course. However, the course should really be titled "Evaluating Classification Methods", as that is pretty much the focus of the entire class. The lectures (especially in Week 2) were SOOOOOO long and very hard to absorb, that even double-speed didn't help. In education, less is more. I would compare this course to the reading of a textbook. There was very little focus on making sense of the code and solving real-world problems and far too much emphasis on shotgunning (what felt like) every single classification technique known to man and trivializing pros and cons of each method. To make matters even more strange, PCA and other useful methods were pushed into "optional". This course should really be a two-part course, especially since the claim is that the course requires 18 hours of time. Sure, type in the code just as the professor does and you get the right answer, but meaning is lost of if you are to adhere to the timeline. If I didn't know more about machine learning and this class had been the first one I had taken, I couldn't run fast enough from the pursuit of a career in this field. Data analysis is intriguing and the methods are varied and fascinating. For me personally, this class was a let-down. Again, I recognize the course was hard work; I am merely stating my personal sentiments.

by Rakesh D

Nov 11, 2019

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