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Вернуться к Applied Machine Learning in Python

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

Оценки: 7,045
Рецензии: 1,283

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

8 сент. 2017 г.

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

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.

Фильтр по:

1051–1075 из 1,262 отзывов о курсе Applied Machine Learning in Python

автор: M V B

9 окт. 2020 г.

It was a great experience learning through Coursera ,who provides best faculty for making students understand easily.

thank you Cousera

автор: Prathmesh D

15 июля 2020 г.

It was a great learning with you all got little problems but solved as per instructions and they helped me through that,thanking you


23 авг. 2020 г.













ience of machine learning using python. Very well explained algorithms and application through modules and assignments.

автор: Dr. P R K

23 янв. 2018 г.

Unlike the name suggests, this course only covers the Supervised learning side of the ML. However, the supervised side is good.

автор: Michael S

29 июня 2019 г.

Everybody has different skill levels, but this was really hard and really, really, really fast.

Did I say it was really fast?

автор: Krishna

22 мая 2019 г.

Course content is very nice and covered aptly. I feel that some where more depth was necessary to understand the algorithms.

автор: bob n

31 авг. 2020 г.

Tough, but fair weekly assessments. Lecturer is a bit on the dry, boring side. Be careful not to let you attention drift.


9 мая 2020 г.

Other than the subtle mistakes, the overall course was very informative. I wish there were more practise exercises though

автор: Mohamed S

26 мар. 2020 г.

A comprehensive course by a wold class university,some teaching could have been better by using more interactive methods.

автор: Amaira Z

12 янв. 2021 г.

Well explained course with good material in python, may be an additionnal week is needed for the unsupervised learning

автор: Ekun K

16 июля 2020 г.

This is a great course. I recommend using the Introduction to Machine Learning book to complement the lecture videos.

автор: Wynona R N

23 июня 2020 г.

Good introduction course on machine learning algorithms. The books and the readings are recommended to look through!

автор: Amanda V C

2 июня 2018 г.

You will learn a lot. But the course is a little bit fast for regular students. Assignments deal with real problems.

автор: Rohith S

16 нояб. 2017 г.

A few more code examples would have helped better understand various packages provided by Python and how to use them

автор: lcy9086

2 февр. 2019 г.

Great course on doing machine learning use sklearn and put little but enough explanation of the theories behind it!

автор: Alexandr S

24 февр. 2019 г.

It would be nice to have more practical assignments like the last one! Anyway it was very interesting! Thank you!

автор: Bharat G

30 авг. 2017 г.

Amazing Course but Please add some more theory and concepts in Neural Networking.Overall it is a good experience.

автор: Alpan A

27 нояб. 2019 г.

Very good curriculum with a hands on project. However thera are some limitations with the platform with grading

автор: am

21 июня 2017 г.

Complete course on supervised learning

Would be nice to cover PCA and unsupervised learning in the assignments

автор: Andres V

16 окт. 2020 г.

the final assignment was too hard compared to the other assignments and the contens given in the last module

автор: CMC

9 февр. 2019 г.

A little dated. Overall a good introduction. The informal explanation of SVM was particularly effective.

автор: divya p

4 сент. 2020 г.

course is very informative with hands on details, assignments and quizzes are very useful for assessment

автор: Maxim P

15 сент. 2018 г.

Nice there could just be a bit more of a case study to see the difference and decision ways in practices

автор: Jesús P S

5 янв. 2018 г.

great course but could be improved with a better explaining of the class on board for abstract concepts.

автор: shashank m

16 июля 2019 г.

Very intuitive course...and carefully designed so that it does not overwhelm the students with details