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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
6,734 个评分
1,212 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

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

筛选依据:

26 - Applied Machine Learning in Python 的 50 个评论(共 1,192 个)

创建者 Krishna B S

Mar 06, 2019

A very comprehensive and hands-on course for learning applied Machine Learning. Many thanks for this course.

创建者 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.

创建者 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.

创建者 eric g

Feb 29, 2020

While I appreciated the difficulty of the course, the poor design and structure of the course is evident with the number of correction pop-ups that come up every video. The professor misspoke countless times over the duration of the course, and there are several typos on the slides that need to be corrected! I feel like the videos were also much more bland than previous courses in this specialization.

Additionally, while feature selection and data cleaning are large components of the final project, they are not at all the focus of what is taught in the course! I think this course was trying to do too much all at once, and leaves you with a shallow understanding of several things instead of a good understanding of any specific thing.

创建者 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.

创建者 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.

创建者 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.

创建者 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.

创建者 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

创建者 Sajjad K

Jul 13, 2020

Teachers are very mediocre. They make way too many mistakes. Their pronunciation is stoic and muffled at times - makes it hard to follow.

创建者 fulvio c

Feb 25, 2020

The video and training provided it's not providing enough information in order to complete the assignments.

创建者 Rakesh D

Nov 11, 2019

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

创建者 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.

创建者 Saqibur R

May 03, 2020

This course is all over the place, and compared to the previous courses in this specialization, this seems like more of an effort to gloss over the documentation and capabilities of SciKit Learn rather than focusing on a handful of the most important ones. The course lacks focus, the material taught is not rich, and you are better off just reading the documentation on your own. The book recommended at the start of the course is excellent, and reading that instead might be more fruitful for you.

创建者 Karim F

Jul 10, 2020

worst course of this specialization so far , the instructor is just reading stuff not making any effort whatsoever and it seems like he's obliged to do teach this course ,the autograder is the worst and the journey with this course is really painful i hope that you take these points in consideration and just delete this course

创建者 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.

创建者 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.

创建者 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.

创建者 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?

创建者 Sudhir K D J

Feb 17, 2020

Very poor configurations. I am tired of submitting assignments on auto grader. This is the first time I am having such terrible experience with Coursera. Hope you improve.

创建者 Marcos B G R

Nov 06, 2018

This is a really bad quality course. A little bit more professionalism would be advisable. I will continue to the next course and leave this behind.

创建者 Rezoanoor/CS/Rezoanoor R

Mar 22, 2020

Faced problem in every assignment while reading the data sets. If the data is not in that folder what is the point of telling so?

创建者 Omid

Sep 22, 2018

1- very slow paced lectures

2- very basic and elementary examples

To sum up, it is boring and not useful for practical application.

创建者 Ipsita D

Apr 20, 2019

No visible support from groups forum. Videos knowledge is limited to complete assignment or quiz.

创建者 Shaoqi C

Mar 10, 2020

This is my worst experience of submitting assignment and I found out that I'm not alone