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學生對 密歇根大学 提供的 Applied Machine Learning in Python 的評價和反饋

4.6
4,644 個評分
803 條評論

課程概述

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....

熱門審閱

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

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751 - Applied Machine Learning in Python 的 775 個評論(共 784 個)

創建者 Sameed K

Mar 15, 2018

have to figure out a lot of things on you own.

創建者 Andy S

Jun 04, 2019

It could have been better with more examples.

創建者 Jeremy D

Jul 10, 2017

The topics were good, but too many were d

創建者 Ryan S

Dec 12, 2017

Homeworks are inconvenient to submit

創建者 Wojciech G

Oct 28, 2017

To fast paced.

創建者 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.

創建者 Taylan T

Oct 20, 2019

TLDR; Boring and unstructured courses that do not offer insight. You learn by doing the assignments.

The video lectures are boring and unstructured. You can tell the lecturer really hates what he is doing often sulking and showing zero enthusiasm. Also, he makes you question if he really knows what he is talking about. I am sure he does but his attitude and sloppy mistakes give you doubt. The format of the video lectures is that the lecturer reads a script in front of the camera and the algorithm he talks about is shown in cutscenes. This is a terrible idea. Also, the courses are not well prepared, lacking continuity. On top of that lecturer often makes mistakes and these mistakes are "corrected" by showing you a cutscene that writes the professor wanted to say X instead of Y. This is really sloppy. This is not an open course where you put your recorded lectures to youtube for free. You are delivering these lectures to paying customers. Seriously many free lectures on youtube are better built compared to these lectures. I have learned a lot of things in these lectures by doing the assignments and trying to learn by using google and not via lectures. One positive thing about this course is that there are some good links to papers, websites etc... But you need a lot of time to go through them.

創建者 Jin-Kyu C

Feb 09, 2020

I would not recommend this course except for week 1. According to some forum posts, not only is this course a bit outdated (needs fixes to many parts and they haven't fixed them for at least 2 years), seemingly small but crucial parts of the assignments are not covered in the lecture videos which were very frustrating and time wasting to figure out (4 week course ended up being 10 weeks for me with a result of 93,4% final grade). Combing through the videos turned out to be futile and of course, relying on external sources such as stackoverflow was also not very helpful since the questions asked need to be extremely specific to the course. Even simply submitting the assignments were met with difficulties; and it's similar forum posts week after week.

創建者 Shiomar S C

Oct 14, 2019

Honestly this course was somehow disappointed I really wanted to learn a lot but the professor was somehow discouraging, he repeated himself a lot, and for an online course and every video been 20+ minutes long and at the end only been useful 4 or 5 min of it… having so much errors during lecture and not following the notebook as it was given to us make it more difficult to learn… I’m choosing this platform (and paying) due the professor been good and this one make learning more difficult than the previous one.

創建者 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.

創建者 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.

創建者 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.

創建者 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.

創建者 Amir A C

Jan 19, 2020

Unfortunately, for me, this course (not the specialization) seems to be a "review of" Applied Machine Learning in Python" rather than "teaching" Applied Machine Learning in Python. Some codes used in the notebook were skipped by the instructor.

創建者 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

創建者 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.

創建者 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.

創建者 Rakesh D

Nov 11, 2019

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

創建者 keshav b

Jan 02, 2020

Instructor tell the thing which are far beyond from asignments and quizes

創建者 Frank A N

Nov 20, 2018

It was too easy

創建者 Gregory B

Jun 14, 2017

I'm disappointed that I took this class, poor design and delivery. Machine Learning is an exciting and fun topic, but you'd never guess it from this class, and the way the instructor delivers the content. It's a shame that the designers want to throw every possible model at you in 1 or 2 weeks, before having a discussion on model evaluation. This course focuses more on the academic than the practical, and doesn't try to explain these topics in an approachable manner. There are far better and engaging options available.

創建者 Rishi R

Jul 06, 2018

Rather then writing code while explaining like the intro and plotting in python, the instructor shows it like slides, its hard to follow which chunk of jupyter notebook he is explaining, and requires lot of back and forth to read the code. Very bad way of explaining the codes.

創建者 Sean D

Jun 12, 2019

This is the worst course in the specialization. The autograder is bad. There is inadequate explanation about when to use the different models. Presumes way too much about the student's level of knowledge. Would not recommend.

創建者 Craig A B

Nov 02, 2018

There's too much back to back to back video lecture and not enough hands on work. The final quizzes and projects are too challenging given the amount of work done on the subject matter.

創建者 Yuchen P

Oct 09, 2017

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?