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学生对 密歇根大学 提供的 Applied Machine Learning in Python 的评价和反馈

4.6
6,196 个评分
1,112 条评论

课程概述

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....

热门审阅

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

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!!

筛选依据:

826 - Applied Machine Learning in Python 的 850 个评论(共 1,094 个)

创建者 Ian R

Nov 11, 2019

I found the course to be a little bit too much of a whirlwind for me to get much more than the broadest strokes out of it. A lot of the topics covered were mentioned very briefly without much explanation of when or how they should be applied - especially week three felt like a barrage of "this exists, this exists, this exists..." without much explanation, and I don't think I'll retain very much of it. The Week 4 assignment, however, was adequately challenging and did give me cause to go back, review and dig deeper into many of the topics covered previously.

创建者 Paulo C

May 03, 2020

Overall a good course! It was really what I was looking for: main focus is on how to apply algorithms and pros and cons of each model, instead of exhaustively explaining the theory behind each one, like some others courses do. The downside was the grade system. The platform has a lot of potential, but crashes all the time and there are many errors to troubleshoot when submitting assignments. The time invested to troubleshoot these problems was really frustrating, and probably the main reason i won't continue with the specialization.

创建者 Amit A

Dec 23, 2019

The course is excellent and Professor Kevyn Collins-Thompson goes to the lengths and breaths to explain various machine learning algorithms and also provides a hands-on the syntaxes for the code to provide a deeper intuition to the problem. The course has a lot of info to be digested and one must go at his/her own pace to grasp all the details. There were some issues with the grader but thanks to the excellent mentors on the decision board, they helped me sort out all the issues. So thanks to the entire team once again.

创建者 yiding y

Jul 01, 2018

Pros:The course provided me with a very good introduction about Machine Learning(in Application level), for example, the relative terms that be using, differences in classification and regression models, the validation metrics and methods, the related tools using in Python. It fulfills the application goal as the Professor said in the week1. I can utilize a lot from the course into my current work. Cons: The auto-grader could be improved better which can save learners lot of time debugging it.....

创建者 Lauren r

May 23, 2020

There's obviously been some reordering of videos that can be confusing and repetitive and the quizzes are not carefully worded which leads to misunderstanding of questions and answers. The material though, unlike in the two previous classes in this specialization, actually help with the assignments so that the assignments help what you learned in the classes. The material is also presented mostly at a reasonable pace (except at the beginning of the second week).

创建者 Rory P

Mar 14, 2018

More detailed videos/maybe case studies on applying the algorithms in real-life jobs would be good. The assignments are generally fairly good, but can be pretty easily cribbed from the course module notebooks. While this is okay since knowing exactly what syntax to write is less important when there are a lot of examples online, it would be good to have the assignments maybe incorporate more thinking about the models and what they mean.

创建者 Sonmitra M

Jan 18, 2020

The course content was good and the assignments were designed brilliantly. I learned more while completing assignments and reading discussion forums. The auto-grader should be improved, it's time wasting and frustrating experience. No response from discussion forums even on technical issues can keep you waiting for weeks unless you solve the issue by your own by reading 2- 3 years old post and meanwhile lost money, time and patience.

创建者 Tesfaye G A

May 05, 2020

first of i would like to say thanks for my Almighty God for being with us all the way we do next i want to extend my thanks and appreciation to Coursera and my applied machine learing professor kevyn collins Thompson, i got this course it is very helpful for every body working on any technology apart from this i want to say a little about the course content that it was very nice if more practice added on it

thank you

创建者 Vidya M S

Sep 09, 2019

Good brief explainataion of supervised algorithm , its working and how its put to use with 'sklearn' . Jupyter notebooks on each module gives you a baseline of how machine learning is done with 'sklearn'. Quiz arent bad either . May be the last assignment on the final analysis of given data to provide a prediction could have been made more challenging by including grade on the EDA and explaination of model results.

创建者 Renier B

Sep 19, 2017

I enjoyed this course. Many people comment on the lack of theory, but I think as important as theory is, it is even more important to be able to practically use ML algorithms.

This course will set you up to start doing Kaggle competitions quite adequately. In fact, the final assignment is very similar to a Kaggle competition and open-ended enough to make you really feel like you need to harness what you've learned.

创建者 Vinayak

Mar 02, 2019

Great course for beginners to start with Machine Learning in python. With sufficient paraphernalia about the concepts, the course dives straight into the guts of ML and helps a lot in applying ML concepts to datasets. The instructor is clear and concise and provides enough auxiliary reading for familiarizing ourself with previously-unknown ML concepts. Thanks to both U Mich and Coursera for organizing this course.

创建者 Nicholas B

Feb 17, 2018

easily the most difficult course in the specialization (so far). learned a lot! Still, the course matter could've been made more clear in some areas of the assignments. Also, the time estimates are way low. Plan to spend 10 hours a week reviewing scikit learn documentation at a bare minimum. I spent over 12-15 hours a week on this course. I STRONGLY recommend if you're looking to get into machine learning.

创建者 Dawid M

Feb 24, 2020

There should be a note at the beginning of the assignment in Week 4, that we may run out of memory with the auto-grader and what to do in advance to avoid that. My biggest time in Week4 was spent looking for and upload umpteen times (trial and error) to find a memory problem instead of upload to learn to calibrate parameters. Received 0.81 (which is rather ok) in the end but the distaste remains.

创建者 Vikram

Oct 17, 2017

Provide a quick and good overview of important, popular machine learning topics and their practical use with Python scikit-learn module. The material covers the important parameters to keep a watch on for performance and highlights the usual pitfalls and missteps. Very practical learning, makes one comfortable using ML tools and quickly apply for real problems like in the last assignment.

创建者 Hritvik S

Jul 13, 2020

The course is designed perfectly and the pace is such that beginners in machine learning would enjoy. The course was well structured out and in a span of 4 weeks I think i learnt a lot. The only limitations i found were with the autograder not detecting files and other minor glitches like the videos not being marked completed even upon completion. But those can be fixed easily.

创建者 jie

Apr 28, 2020

Just like other couses in this specialization, this course has great assignments which help alot.

As to instruction, totally different to previous courses, this instructor covered almost everything, probably too much for a four week course. I think I start to have some sense of machine learning however, I do need more study, probably Andrew Ng's course and refresh my maths.

创建者 Maxwell's D

Jun 23, 2017

I really got a lot out of this course. I started with a solid background in traditional data analysis (PhD in experimental physics), but knew nothing about ML. This was a great overview, providing a just the right trade off between depth and breadth--plus it was short, which is good. I can now go and do deeper dives into the material. Thank you!

创建者 Maurizio

Jun 06, 2019

I think it gives a great overview on Machine Learning and Sklearn. Nonetheless i noticed it is less curated compared to the prevoius courses in this specialization (wrong filenames, unfunctioning links, old version of pandas respect the one used till now). Anyway it worthed and I'll give a look also at the optional unsupervised learning part

创建者 Çağdaş Y

Oct 22, 2017

The teacher's voice is not motivating, it made me fall asleep all the time. But content is surely good. It's a perfect checkpoint after Andrew Ng's machine learning courses, by making experimental practices over theoric practices. Seriously, speaker needs to speak more alive! I don't want to hear deep breathe noises when watching a course :)

创建者 Mohit K

May 24, 2019

I Took this course blindly without knowing much about data visualization libraries. It took me a month or so to learn them first and then attempt this course further. The course study material is very decent but the assignments are pretty good and tricky. It is definitely a must-go-for course and I would surely recommend to my colleagues.

创建者 Samchuk D

May 30, 2018

This one is very good and informative.

Although there is no explanations how to decide what type of preprocessing do on data set (to choose whether or not to do winsorization, convert categorical features to one-hot for linear models and to labeled for trees, etc) it still very helpful in understanding of PRACTICAL part of machine learning

创建者 Sridhar V

Jun 12, 2020

This course was very interesting. Probably the longest course (duration wise) in this specialization. This course had to cover a lot of ground in 4 weeks time. Thoroughly enjoyed the assignments and it was challenging as well!. Gave 4 star because there are minor problems wrt. Autograder. But content wise there are no complains.

创建者 Narendhiran

Feb 16, 2020

Lectures were a bit slow, I personally felt pace could be increased and more content could be covered in areas like boosting and all.The assignments gave me a hands-on approach in using sklearn library.I felt it was over-all a very good course and would definitely recommend it for others.

Thank You

Yours sincerely,

Narendhiran.R

创建者 Chaitanya D

Jul 04, 2017

Interesting course, was curious about what all things will be covered in this course. It touches most of the topics that one should be aware of ML. Only thing that I felt bit overwhelming was the amount of material which was covered in 4 weeks. Could easily be stretched to 5/6 to make it less demanding for a novice person.

创建者 Marcin B

May 26, 2020

Good stuff :) However approaching final assignments I was missing more info about preparation of an input data. As far as I know it is to some extent covered by first course of entire Specialization. So, I plan to take this one as well. But overall - very good intro to ML in my view. Thumbs up University of Michigan :)