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

4.6
звезд
Оценки: 7,147
Рецензии: 1,297

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

AS
26 нояб. 2020 г.

great experience and learning lots of technique to apply on real world data, and get important and insightful information from raw data. motivated to proceed further in this domain and course as well.

FL
13 окт. 2017 г.

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!!

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1001–1025 из 1,276 отзывов о курсе Applied Machine Learning in Python

автор: Mohit K

24 мая 2019 г.

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.

автор: Dmytro S

30 мая 2018 г.

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

автор: Sridhar V

12 июня 2020 г.

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.

автор: Narendhiran

16 февр. 2020 г.

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

автор: Chaitanya D

4 июля 2017 г.

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.

автор: Marcin B

26 мая 2020 г.

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 :)

автор: Alan E

5 февр. 2018 г.

Great course, with a very practical overview of the different options available for machine learning models using Python. The concepts are the same as in R-based machine learning, but this course was great for getting experience with which Python functions to use for various machine learning models.

автор: KUMAR M

25 нояб. 2019 г.

Great course. It doesn't confuses you very deep mathematics involved in machine learning. Rather, with a touch of it, it focus more on how and when to apply the models in Machine learning. How to evaluate and optimize them. It's really Fantastic with it's hands on projects in assignments.

автор: Elizaveta P

15 мая 2018 г.

This course is very cool and interesting. One thing, it would be more useful for me to have a little test/exercise after or in the middle of every video - to try, how I understood the material. Like in Andrew NG course or in Text Mining.

Anyway, thanks for a great course and your work!

автор: Amina B

12 июня 2020 г.

Great course, somehow assignments are not always on the same level, the first was easy, the last seemed to be very complex, but was not, the assignment instructions were misleading. Anyway, I enjoyed this course too much and I want now to improve my abilities in underlying theories.

автор: Lalitha G

5 нояб. 2019 г.

Not only in the last week, all the weeks can have assignments which are like projects. That may give more sense of analyzing and understanding the process of model selection, application of supervised learning techniques. But the course is good, and i have learnt it in faster pace.

автор: Lu E

7 нояб. 2017 г.

kind of a good course. However, I think too much things have been put into this four-week class. All methods, for example, random forest method need a lot of practice. In the four week, I think I am not familiar with most of these method and I need to practice more in the future.

автор: Bret

16 июня 2017 г.

This was a very practical course with a lot of useful stuff! My main frustration was that the final assignment could have used more starter code, as I spent way more time trying to get the data to load properly than I did on finding a model to score high enough for full marks

автор: saikanth g

13 апр. 2020 г.

Totally nice course,As it is Applied Machine Learning all lectures do not go deep and just touch on the topics.Did not face any issue with autograder this time but its better to use newest version of jupyter notebook.The teaching staff were highly responsive.

автор: Gaurav

8 июня 2020 г.

The course was really well constructed, but there wasn't much to teach in it like just use this code and get the values.

I strongly feel that all the assignments should have been like the assignment of week 4.

None the less, it was a great learning experience.

автор: Daniel W

9 июля 2017 г.

Pretty good. I really like the quality of the notebooks provided. Also assignments are interesting.

I would improve quizzes. Some questions were really hard to understand or misleading.

Also, I would really love to learn more in depth about the algorithms.

автор: Amit P

26 дек. 2019 г.

This course is an excellent run through of the pipeline for developing, running and evaluating machine learning models. The video lectures were monotonous and long, though. The last assignment was especially meaningful and enjoyable. Highly recommended.

автор: Donald V

17 дек. 2017 г.

If I could I would give this course 3.5 stars. Most of the coverage of the concepts in this course were pretty light and there were several issues with the autograder being difficult that made this course a lot less enjoyable than it could have been.

автор: tanuj

8 сент. 2020 г.

There were a few mistakes in the assignments which causes unnecessary time wastage on student's end. Otherwise, it was quite a good course.

Also including a demonstration of encoding textual data while implementing Random Forest would be helpful.

автор: Cole M

30 авг. 2020 г.

Good practice content and good explanations. Some of the content I would rate as great. There could have been more smaller programming exercises that built up to the main exercise for each week. This is the only reason I did not rate as 5 stars

автор: Al W

18 нояб. 2019 г.

Lots of minor issues with the Jupyter notebooks that could easily be fixed but the instructors just post a way to solve the problems in the discussion form instead which is frustrating. The material itself was extremely interesting and useful!

автор: Siddharth S

11 июня 2018 г.

It would have been wonderful if the notebook codes were written and explained in the video the same way as in earlier courses in specialisation taking care of the implementation details as well.However still a Good Course of the Specialisation.

автор: Varada G

22 июля 2017 г.

It is a bit dense - be prepared to spend more time working through examples - and reading the reference book. The lectures, unlike the previous ones in this set, does not allow time for you to practice with the examples in jupyter notebook.

автор: Sparsh B

8 июня 2020 г.

This course was really helpful in understanding the working of various machine learning algorithms.

I was able to gain understanding of various evaluation techniques and there usage in different scenarios.

Thank you for this wonderful course

автор: Mark S

1 сент. 2020 г.

Lots of useful information, but sometimes the content could have been better explained. Too many errata than necessary in the assignments at the end of each week. I found that the Jupyter notebook would stop working after about an hour.