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Отзывы учащихся о курсе Applied Machine Learning in Python от партнера Мичиганский университет

4.6
звезд
Оценки: 5,388
Рецензии: 950

О курсе

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....

Лучшие рецензии

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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26–50 из 932 отзывов о курсе Applied Machine Learning in Python

автор: 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 According to some forum posts, not only is this course a bit outdated (needs fixes to many parts and they haven't fixed them for at least 2 years), seemingly small but crucial parts of the assignments are not covered in the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.

автор: 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.

автор: 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.

автор: GUOJUN W

Apr 08, 2020

I know It is a hard subject to teach, but many ways to improve. Students could have been able to understand those concepts much better by using common or popular topics for assignment cases or practices. Without context, many useful concepts taught are forgotten right away. Lecturer should have not explain those concepts by simply talking to the camera without illustration or vivid examples. Often the way lecturer speaks and teaches is quite boring.

автор: 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.

автор: 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.

автор: 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.

автор: 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

автор: fulvio c

Feb 25, 2020

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

автор: Rakesh D

Nov 11, 2019

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

автор: keshav b

Jan 02, 2020

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

автор: Mohamed R

Mar 28, 2020

one of the worst courses i ever had

автор: Frank A N

Nov 20, 2018

It was too easy

автор: 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.

автор: 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.

автор: 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.

автор: 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.

автор: 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.

автор: 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?

автор: 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.

автор: 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.

автор: Andriy A

Aug 13, 2019

A lot of bugs in Assignments. Instead of learning ML need to go though forums/code to fix simple bugs like files locations readonly/ etc

автор: 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?

автор: 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.

автор: Ipsita D

Apr 20, 2019

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