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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
4,800 个评分
836 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:

1 - Applied Machine Learning in Python 的 25 个评论(共 820 个)

创建者 Brendan B

Jan 06, 2019

Glosses over material (much like prior courses in this specialization), the professor is audibly nervous during recorded lectures, and many assignments require information and functions not covered in the lectures. Additionally, out of date Python modules are used in the notebooks, so you're learning often deprecated usage patterns, not to mention the constant struggle that is the auto-grader. You can teach yourself with free resources and save yourself the money and unhelpful bouts of rage against the auto-grader.

创建者 Sarah H H

May 02, 2019

I want to give this course a higher score because I do think I learned A TON. However, I learned a ton because the course had some flaws in instructions and assignments that required some frustrating moments and a lot of outside work to correct. If you take this course, DISCUSSION FORUMS are a must because of all the errors and bugs in assignments. The explanations are a little 'too rosy' in the videos in my opinion (they show best case scenarios) so there's a disconnect in what i actually had to do to pass the assignments which tended to have lots of room for improvement. That said, if you are willing to go out on your own and figure it out (mentors are so-so in actually helping), then this course is a great ML workout!

创建者 solarmew

Jun 13, 2017

Not very good compared to the first two courses :( :( :( ... I took a Machine Learning Class from Stanford which was incredibly well put together and presented (though to be fair, it was 12 weeks), but it was in MatLab and I wanted to take a course in Python just to have a different perspective and solidify my understanding. Unfortunately, I find this course to be confusing more than anything. If I hadn't taken the Stanford course before, I'd be completely lost. It's very dry, dense, and hand-wavy and doesn't go into a whole lot of details with anything leaving you wondering what's happening and why and how... I don't approve of jumping straight to using the built-in functions if you don't understand the processes behind them (which I personally don't have a solid grasp on them still) ... I think they are just trying to fit too much information into four weeks and it's really lacking. Maybe if you're already familiar with linear regression, it's not as hard to follow. Either way, I'd recommend either taking the Stanford class first, or learning about this stuff elsewhere before starting this course.

创建者 Riccardo T

Sep 21, 2018

A lot of stuff, compressed in a short time. It's more about memorizing a lot of concepts rather than understanding them. I strongly recommend to take the course of professor Andrew Ng before this one.

创建者 Max B

Jan 03, 2019

This is a great course for those with limited experience of machine learning, wishing to quickly grasp how to apply machine learning methods and get their hands dirty. In my opinion, this is the best course in the specialization so far and as in previous courses you are expected to dig into further theoretical/usage details yourself from online documentation (hence the name applied). Concise lectures and interesting reading materials, as well as hands-on assignments. My recommendation is to either start with this course or take it together with more theoretical courses (such as "Machine Learning" from Stanford or "Machine Learning Fundamentals" from UCSD) to get the full flavour of what machine learning has to offer.

创建者 Lin Y

Jul 09, 2019

This could the single most interesting course amongst all the 5 courses in this specialization. It made Machine Learning easy to interpret and fun to explore for beginners. The assignments are very thorough, though with some autograder issues. I strongly recommend anyone who's interested in ML to take this introductory course to again some knowledge in the different methods and applications of ML in various fields.

创建者 Olzhas A

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

创建者 Haim S R

Jun 27, 2019

Gives practical experience with ML in Python.

Hides the math under the hood :(

However, this course is not enough to become a real data scientist. One needs much more exercises.

创建者 Aziz J

Nov 07, 2017

My biggest critique of this class is that it is not challenging at all. Homework assignments are just a repeat of the lectures and take less than an hour if you took notes on the lectures. In other words, there is no value in the homework assignments.

The first two courses in this specialization were awesome. We did real life examples for homework assignments and through research you learned more than you had asked for. It was perfect.

Even in lectures, there is nothing 'applied' about this course. The professor just covers the content with no real-life examples. Very mundane and unexciting.

Also, why not talk about multi-label classification? Professor takes a real example with multiple labels (handwritten digits), makes it a binary class and then proceeds to explain it... Thanks.

My recommendation would be to restructure the homework assignments. Instead of having 7 questions that spoon-fed you the solution of a primitive problem, ask us to do some Kaggle challenges, or give us a topic that we go out and solve, do some peer-reviewed assignment. Lastly, if you don't have time or don't want to explain important concepts like pipeline, nested cross validation, and multi-label classification, add them as resources.

I am NOT confident in my ability to solve machine learning problems in Python from this course, nor is this course worth recommending.

创建者 Arpan S

Aug 04, 2019

I am unenrolling because of the following reasons:

1) instructor lead training is very very boring - the gentleman keeps talking in same pitch and there is no lucid explanation behind the math that is constantly thrown at you

2) the course does not bother to put in any real world scenarios to correlate the content with

Overall really poor experience

创建者 SeyedAlireza K

Nov 17, 2018

There is a huge difference between teaching / tutoring and just reading some pre-written scripts. Even on an online course. Andrew Ng's Machine Learning course is a great example of teaching and this was one of the worst courses I have ever taken in coursera / udacity.

创建者 Vaibhav S

Jun 26, 2018

This course provides a brief introduction to many of the vast and dense ML concepts, like Regression, Classification, Clustering, Neural Networks and many more.I took a course by Prof. Andrew ng on Coursera before taking this course. And due to this reason, i was somewhat familiar with the concepts that are being taught in this video.If you are a beginner, i personally recommend you to take Prof. Ng's course on Machine Learning, and then switch to this part of specialisation, by completing the 1st specialisation (2nd is optional but if you are sort of artistic person, and have a habit of visualising things then opt this too). It is best for those who just want a quick recap of some topic.

创建者 Frank L

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!!

创建者 Athira C

Jan 30, 2019

The course is so informative and interseting.

创建者 Oliverio J S J

Feb 04, 2018

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.

创建者 Raivis J

Jul 27, 2018

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.

创建者 Choi H

Nov 23, 2018

어려웠어요 ㅠㅠ

创建者 Shikhar S

Jun 03, 2019

The content of teaching is a way too less than the assignment's level. I had to make efforts on my own .

Kindly increase the content of teaching.

创建者 Kevin L

Jun 25, 2017

A great introduction to the practical side of machine learning, particularly if you have already taken Andrew Ng's course. It covers a *lot* of material and the pacing is *very* fast. Week 2 is particularly long, and if you are still a student/working it may take an extra week to complete the course. Quizzes and assignments are not terribly difficult, but be careful of the project assignment in Week 4 (though the bar for a 100% is quite low!). Finally, the accompanying Jupyter Notebooks are very helpful and there are many helpful links to outside resources as well.

A few of the lecture videos feel like an early draft rather than production-quality, with lots of time spent on repeating phrases. The instructor mentions things to be covered "later," but that "later" never comes (for example, in discussing Grid Search). For some background, this course appears to have been repeatedly delayed before its release. To me, is understandable that the creators wanted to get this course out given the demand, but the rush is felt.

Ultimately, however, this is still an excellent introduction to Python Machine Learning, and I do feel the course is well worth taking. Just be prepared to do some more individual learning; however, shouldn't one always be for an online class?)

创建者 Luis G A B

Apr 12, 2019

Muy agradecido, mis felicitaciones al Profesor Collins-Thompson, se muestra como una persona amable, dinámica y con alto grado de conocimiento, gracias a sus enseñanzas estoy aprendiendo más sobre el proceso de machine learning, siento que aun me falta mucho por recorrer, sin embargo, a lo largo de este curso aprendí los métodos, tipos de modelos, herramientas tanto para clasificación como regresión enfocándome en el área. De igual forma la literatura es muy interesante, se encuentran artículos que al leerlos vas comprendiendo como ha sido el proceso de transformación en este campo y gracias a esto, se me han ocurrido ideas que me gustaría compartir o estructurar para evidenciarlas de manera mas formal.

Muchas gracias por el apoyo, gracias por las observaciones y anotaciones dentro de los foros de discusión, siento que puedo seguir aprendiendo mas y es por eso que estoy agradecido por mis conocimientos adquiridos, los cuales siempre puedo retroalimentar viendo el curso nuevamente cada vez que lo considere pertinente.

创建者 Stephen K

Oct 03, 2019

5 starts for content. The lecturer and slides were good. The assignments were often difficult and took many hours longer than the stated 3-4 hours. Assignment 4 was particularly heavy in time. I finished the course feeling equipped and confident enough to take on straightforward machine learning projects from start to finish. I've dropped a star because the autograder uses an older version of Python and older libraries, which meant I had to spend around 8 hours re-engineering my *correct* code to conform to old libraries.

Addendum: I've uprated the course to 5 stars after having just completed the fifth, optional week on unsupervised learning. It's unassessed but does give a nice introduction to the subject. Thanks!

创建者 Jack O

Apr 25, 2018

Though I would have liked a bit more insight into the actual algorithms behind machine learning, this class did a great job of giving us problems and forcing us to be resourceful and hunt down the answers, whether via course forums, Stack Overflow or other random Googling. We were exposed to a ton of different algorithms and libraries, and we got to experience the whole spectrum of data science: data importing, cleaning, exploratory analysis, feature selection, model selection, parameter tweaking and even some visualization. It was a lot of fun: challenging at times, but oh so rewarding in the end!

创建者 Anne E

Feb 14, 2019

Very nice class for people who have some intermediate knowledge in Python and who want to dig in, or consolidate their knowledge in Machine Learning. Great overview over scikit-learn, also going into details, and I also appreciated the part of the class about model evaluation. First week might seem not overly difficult, but the intensity of the class ramps up significantly in week 2. For me the level was challenging enough, without being overwhelming. I enjoyed taking this class and obtaining my certification at the end was a very nice reward. A big thank you to University of Michigan.

创建者 Bart T C

Oct 10, 2018

This course is excellent. It contains a great deal of instruction each week (1-2 hours), and it also has many supplemental references for people who want to go deeper. The quizzes are actually very challenging, and require study of the material. The assignments were easier for me than the other courses in this specialization, but they were focused on application of the material to real world problem, which is the purpose of the course. The final assignment is very instructive and challenging. The instructor is very knowledgeable, and teaches in a thorough, but easy to follow, manner.

创建者 Tsz W K

Jun 09, 2017

I completed the Machine Learning Specialization Certificate before taking this course. This course is an excellent applied course that quickly gets into the key aspects of using sklearn. This course is ideal for both new learners and experienced learners who just want to learn more/revise about machine learning. For the final assignment, it requires substantial data cleaning techniques covered in Course 1 in this specialisation. Overall, I feel very comfortable with using Python for any reasonable size of machine learning problems after taking this course.