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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
8,020 个评分
1,464 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 8, 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 13, 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:

1351 - Applied Machine Learning in Python 的 1375 个评论(共 1,456 个)

创建者 Dimos G

Sep 3, 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.

创建者 David M

Oct 19, 2018

The quality of the teaching is a marked improvement over module 1 & 2 in this specialisation. In my opinion it would be a 4/5 star course on that alone however there is 1 minor and 1 major issue. Starting small, the course could do with better summary notes/cheatsheets to help remember details and as prompts when doing assignments; I found it really annoying to have to skim read the lecture video transcript or scan through the videos. The MAJOR issue is the problem sets and the autograder. I really feel the teachers need to re-write this whole section before I could recommend this course.

创建者 Gu X

Oct 19, 2017

Most of the content professor taught are intuitive, but the PPT seems helpless. Furthermore, the thinks in the course are shallow depth, conversely the assignment are little bit difficult especially on assignment4. I mean if the goal is to train our to do some real world data you may can shrink the dataset, the large dataset would takes more time to training which would cost more time to debug. Anyway, this is a great course but I think it's better to do slight change on the quiz and assignment.

创建者 Pablo S

Aug 21, 2020

This is a good introduction to applied machine learning with python. Although it is "applied" it would be worth to cover the basics of the presented algorithms a bit more thoroughly. In paticular, I think that the regularizations parameters and their role in bias and variance are not presented in a very clear way. On a different topic I think that the course deserves to be updated with latest sklearn implementations and correct a lot of bugs in the assignements and lectures.

创建者 Melanie B

Jul 17, 2017

This course helped me to get started on using Python for machine learning tasks.

Personally I would have preferred a more mathematical approach when discussing the various machine learning techniques, in order to learn more about what's going on "under the hood" in scikit-learn. I know that the course is called "Applied Machine Learning in Python", but to me it felt more like "Extremely Applied Machine Learning in Python" :-) Other than that, I enjoyed this course!

创建者 Carl W S

Jul 2, 2017

There is a lot of good material in this course, but it is noticeably not taught as well as the previous two courses in this specialization. The lesson plan feels like a class lecture modified just barely enough to work as a MOOC, the autograders are highly finicky, and most of the programming assignments had errors or missing details that required the learner to check the class forums to find out how to fix them. Overall, it was a helpful course, but felt unpolished.

创建者 Daniel K

May 24, 2020

I do think the lectures are very well done and I believe I learned a lot. However the programming assignments part was frustrating, there are a lot of issues with the autograder, loading files etc. I would appreciate if the steps were described in greater detail. Some parts are very easy, just blending together a few pieces of code from the lecture, and others very difficult, built on things not covered in the lectures. The last assignment was the perfect balance.

创建者 Tamás M

Jul 7, 2022

I liked the material, learning about lot of topics in machine learning. Not in depth, but that is fine.

The negative sides:

- Old Python and scikit-learn versions. This made it some times annoying to work with assignments, that I couldn't use later features and had to refer to old documentation.

- The videos were "dry". There are lot of videos in a row without any interactive tasks inbetween. This made it more difficult to focus for me, compared to other courses.

创建者 Thiti C

May 31, 2020

This course is, in fact, excellent. One can learn a number of algorithms used in a machine learning practically. This course does not focus much on mathematics behind tools we used, the professor taught a lot about the practical one. However, some of the parst in this course are too rush; you have to understand a lot of concepts in Python berfore entering this course, including basic Python syntaxes, and practical libraries such as Numpy and Pandas.

创建者 Adithyan U

Jul 3, 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.

创建者 Charles L

Mar 18, 2020

The material seemed ok. Really annoying that this course genuinely had incorrect code in the homework assignments. It seems that some documents changed directory and were different in the homework folders, vs the grading tool. resulting in failed grades where tests worked just fine. Easily fixed, but why would I have to? Really hurts the notoriety and reputation of this program to have such simple frustrating errors. (on 3 of 4 assignments!)

创建者 Amit S

Apr 14, 2019

It would be better if this course was not with Jupyter notebooks. Professional data science projects will not use notebooks but script files instead. The course should prepare students for professional projects by using script files.

Also the lecturing is very rigid and scripted which makes it less engaging. There is also no material on how any of the algorithms work in detail however there is good material on scikit-learn.

创建者 Koo H S

Mar 8, 2020

While the course material is very helpful and reasonably pace, I felt like I'm always battling the autograder to pass the assignment. I do think that I spend more time to get my answer accepted by the autograder than actually working on the assignment itself. I think an easy way to fix this is to clearly layout the tips to get pass the autograder, rather than having the students to search through the forum for a solution.

创建者 Joseph D P

Nov 14, 2017

I feel like the assignments for this class were very lacking compared to the other courses in this specialization. They were glorified code copy and pasting and didn't make you learn much. There was much more video instruction than in the other courses in this specialization, though. Definitely would recommend reading the accompanying O'Reily book to help you understand the difficult concepts better.

创建者 Eric M

Jun 29, 2017

I learned a lot from this course, but I do not feel like I truly understand everything. There was an extraordinary amount of information that made it difficult to keep on track and take everything in, not to mention apply the concepts in the assignments. I feel confident with the concepts and I could do much better in the future with more practice with skills developed from this course.

创建者 Anand M

Jun 22, 2020

The course is good but I expected a faster response for my regarding assignments & course materials .

Can you guys make sure that your mentors reply faster to student queries ?

You need to make assignments more descriptive as lot of time is being spent on forums to just understand the problem correctly.

The autograder behaves erratically lot of times so you need to make it more efficient.

创建者 Sourav P

Oct 27, 2018

Nothing wrong really. Should have provided more mathematical theory in the resources section.

Assignments should be a lot tougher and on real life data sets which require recodings and transformations. Quizzes should be more relevant to the lessons taught. More hardcore theoretical resources, like books and research papers should be included in order to complement the practical lessons.

创建者 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.

创建者 Fatemeh M

Nov 25, 2018

Hi

First I want to thank all the instructors and anybody that was involved in this course preparation. That was a great opportunity and I really liked that but not in all parts . I think the syllabus was a little heavy and somehow I couldn't follow that . in the programming part I needed more guide and sample .

But in general It was good and I thank you so much.

创建者 Bhavesh B

Jul 7, 2021

The course was great. The only drawback was that the faculty did not feel confident to give video lectures. The videos were crisp, to the point and explained all the concepts beautifully. My only suggestion to the faculty is to record the audio clearly, as sometimes things were not clear and it became necessary to go back and watch the videos again.

创建者 Max W

Apr 24, 2021

Excellent but basic explanation of algorithms. The sample code is useful. If I did not refer to outside resources I am fairly sure I would not have been able to complete this course. The autograder really needs some work. Overall, though, I learned something about these algorithms and appreciate the effort put into preparing this course.

创建者 Robert S

Nov 1, 2020

The subject matter is interesting, but there are many issues with the assignments that should have been fixed before the course is offered, for example, unworkable code segments that remain in the assignments or that prevent the grader from functioning properly. Be sure to read the forum carefully before beginning coding assignments.

创建者 Mario P

Dec 8, 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.

创建者 Vatsal K K

May 24, 2020

I think the instructor must give more practical explanation for scikit-learn. I need to research almost everything for completing a particular assignment. Please have changes in pitch of your voice while delivering the lectures so the lectures don't seem boring. Also, please update the autograder !

Overall a good course. Thank you.

创建者 MD T R J

Apr 12, 2020

The course material is good, but the teaching style is too boring. Without the standstill slides, if there is animation, it would be helpful for us. And, the assignments are not straight-forward and the autograder is buggy. As an example, I can run the assignments easily in the jupyter, but the autograder faces problems.