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Вернуться к Applied Machine Learning in Python

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

4.6
звезд
Оценки: 6,722
Рецензии: 1,207

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!!

OA
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

Фильтр по:

976–1000 из 1,189 отзывов о курсе Applied Machine Learning in Python

автор: Eugene S

3 июля 2017 г.

Automatic assignment grader has room for improvement. Some python code that works perfectly well when run locally or on the course web page would crash when run by autograder.

автор: Jiunjiun M

7 мар. 2018 г.

The class material is well prepared and make machine learning very easy to learn. The first three homework assignment is a bit hand-holding but the last one is really good.

автор: aminedirhoussi

22 окт. 2019 г.

Good Course, i would have liked a little bit more theory about the algorithms, but this is an applied course of ML. Projects are good and the readings are interessting!

автор: Gautam P

20 нояб. 2017 г.

Videos are good and had challenging assignments. I enjoyed learning new concepts. I wish we had one more week to practice more on advanced Machine learning concepts.

автор: Giovanni S

16 июня 2020 г.

Very interesting, a lot of focus of statistica theory and little less (as compared to previous courses of specialization) on practical examples and implementation.

автор: Jiangzhou F

23 июня 2020 г.

Good overall but some concepts and python functions need more explanations. Maybe 5 or 6 weeks are more appropriate for this course. It is too dense under 4 week.

автор: Holden L

31 авг. 2019 г.

better than the first two courses of this specialization for the content is coherent and the assignment is relevant to the knowledge taught in the course video.

автор: Leon V

2 июля 2017 г.

Request: Can we have the instructions with a "translation" to "regular" English - for those of us who still have to get used to machine learning jargon? Thanks.

автор: Christian P

5 авг. 2019 г.

Code and examples were very useful. Teaching a bit lengthy and detailed at times. Overall a very good course for getting hands-on machine learning in python.

автор: Weiqi Y

24 окт. 2017 г.

It's alright as a course focusing on applied techniques. If you are expecting more theories and understanding of the algorithms, this one may not for you

автор: Helen L

15 июня 2020 г.

Submission isnt easy often gave errors that are not due to students' faults. Time-consuming unnecessarily. The content and assignments are great.

автор: Utkarsh S

22 июня 2020 г.

Very informative course, the only issue I had was with the file locations in the assignments. Takes up a lot of time switching back and forth.

автор: Mariano T

18 мая 2020 г.

There are some problems with the assignments but the course is very good. You must improve the material for the assiggnment. I love the forum

автор: Srinivas K R

22 сент. 2017 г.

Good overview of machine learning topics with practical exercises in the use of multiple techniques primarily through use of scikit-learn.

автор: David W

3 июля 2017 г.

Hands on and practical. Dr. CT and his staff have done a great job introducing Machine Learning. Where were you 20 years ago? Thank you!

автор: Rakshit T

10 июля 2018 г.

A good course for beginners in Machine Learning. You get to the learn the basics of many techniques and their implementation in python.

автор: yannick t

12 апр. 2018 г.

Excellent lectures. However, I would have needed more guidance for the last assignment. I learned a lot, but through pain and struggle.

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

автор: PRATIKKUMAR A P

22 авг. 2020 г.

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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.

автор: BHAGYASHREE B

9 мая 2020 г.

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