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

4.6
звезд
Оценки: 7,251
Рецензии: 1,319

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
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!!

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.

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1001–1025 из 1,296 отзывов о курсе Applied Machine Learning in Python

автор: Mohammadmoein T

2 янв. 2021 г.

I did learn a lot from this course and its exercises. I believe it can be a good start for beginners in Machine learning. You might have to do lots of googling to figure out a few tricks in the assignments, but that only makes you a better learner. I wish the instructor didn't just read texts from the screen. There were a few mistakes in some of the lectures, but overall I'm very happy about my achievement.

автор: Dhanush b s

30 авг. 2020 г.

Many core concepts were not given much importance in the videos. The teacher talked in a very monotonous way and was literally reading from a script. Found myself going to several websites and the prescribed book most of the time.

But the final assignment really validated our work by giving us the opportunity to solve a problem all on our own without many hints.

Overall: Teacher- bad, course material-good

автор: Dawid M

24 февр. 2020 г.

There should be a note at the beginning of the assignment in Week 4, that we may run out of memory with the auto-grader and what to do in advance to avoid that. My biggest time in Week4 was spent looking for and upload umpteen times (trial and error) to find a memory problem instead of upload to learn to calibrate parameters. Received 0.81 (which is rather ok) in the end but the distaste remains.

автор: Vincent R

23 янв. 2021 г.

The course is a good introduction to ML. It covers lots of basic supervised ML techniques. The lecture slow pace is appropriate for presenting complex issues. It would have been beneficial to spend more time on the python case studies that are barely explained. Coursera platform issues with submitting and grading assignments should be highlighted in the assignments; not embedded in the forum.

автор: Vikram

17 окт. 2017 г.

Provide a quick and good overview of important, popular machine learning topics and their practical use with Python scikit-learn module. The material covers the important parameters to keep a watch on for performance and highlights the usual pitfalls and missteps. Very practical learning, makes one comfortable using ML tools and quickly apply for real problems like in the last assignment.

автор: Hritvik S

13 июля 2020 г.

The course is designed perfectly and the pace is such that beginners in machine learning would enjoy. The course was well structured out and in a span of 4 weeks I think i learnt a lot. The only limitations i found were with the autograder not detecting files and other minor glitches like the videos not being marked completed even upon completion. But those can be fixed easily.

автор: jie

28 апр. 2020 г.

Just like other couses in this specialization, this course has great assignments which help alot.

As to instruction, totally different to previous courses, this instructor covered almost everything, probably too much for a four week course. I think I start to have some sense of machine learning however, I do need more study, probably Andrew Ng's course and refresh my maths.

автор: Maxwell's D

23 июня 2017 г.

I really got a lot out of this course. I started with a solid background in traditional data analysis (PhD in experimental physics), but knew nothing about ML. This was a great overview, providing a just the right trade off between depth and breadth--plus it was short, which is good. I can now go and do deeper dives into the material. Thank you!

автор: Felix H

16 янв. 2021 г.

The combination of assignments and lectures worked niceley for me. Good feedback on the discussion forums, too. Only thing which should be improved is the auto grader. The course introduces a lot of algorithms, but also gives you insight into how to evaluate their performance. In the final assignment it all comes together, which is always nice :-)

автор: Maurizio

6 июня 2019 г.

I think it gives a great overview on Machine Learning and Sklearn. Nonetheless i noticed it is less curated compared to the prevoius courses in this specialization (wrong filenames, unfunctioning links, old version of pandas respect the one used till now). Anyway it worthed and I'll give a look also at the optional unsupervised learning part

автор: Çağdaş Y

22 окт. 2017 г.

The teacher's voice is not motivating, it made me fall asleep all the time. But content is surely good. It's a perfect checkpoint after Andrew Ng's machine learning courses, by making experimental practices over theoric practices. Seriously, speaker needs to speak more alive! I don't want to hear deep breathe noises when watching a course :)

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.