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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: 6,859
Рецензии: 1,240

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

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

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!!

Фильтр по:

1176–1200 из 1,221 отзывов о курсе Applied Machine Learning in Python

автор: Jan

7 авг. 2017 г.

Quick tutorial-like overview. Autograder is not too verbose and as a result I spent some time struggling with debugging the code rather than figuring out how to solve machine learning related problems.

автор: Fernanda T

4 авг. 2020 г.

Good content and I learned a lot. However, the instructor made too many mistakes during the lectures and the assignments also have mistakes that need to be fixed by the students.

автор: Ketan L

4 июня 2018 г.

Follow the course with introduction to ML with python to have descent understanding. Instructor won't be able to keep one interested for long. Exercises could have been tougher.

автор: Victor E

16 авг. 2017 г.

Two point: 1) you can learn a lot here, 2) imagine you are shown a hammer but never explained how to hit a nail. Two previous courses in the specialization do both.

автор: Kareem H

3 мар. 2020 г.

Course instrutor and materials are needed to be improved as they are very poor

Assigments\Quizes are very good and they are the mainly root cause for this rating

автор: Thomas B

7 июля 2018 г.

Some very good practical advice like dummy testing or data leakage issues Some trivialities and repetitions. Python code could have been a bit better commented

автор: BIRENDRA H S

13 июня 2020 г.

there should be some low level usage of sentences for a intermediate programmers,most of times it bounces up the mind ,not able to get the required concept

автор: Baizhu

5 июля 2017 г.

Know some existing machine learning functions and packages from sklearn, but really don't know how to improve prediction accuracy within each function.

автор: Matteo B

10 авг. 2019 г.

Assignments are not really supported by the material provided (videos). The level is not balanced. Some bugs in the assignment code as well

автор: Berkay A

15 июля 2020 г.

This course seems hard and actually I did not like the syllabus so much. Assignments were so hard and there were some issues in Notebooks.

автор: Halil K

26 сент. 2019 г.

Good content, bad teachng staff. Though the discussion forum contributors were very helpful and should be commended for their efforts.

автор: Ankur P

30 мар. 2019 г.

Unsupervised learning was missing. The codes written in the lectures were not explained clearly. Some topics looked unimportant.

автор: James F

13 февр. 2018 г.

Good overview of methods. A bit too intense at times though, may have been better to really focus on a couple of key concepts.

автор: Om R

26 апр. 2020 г.

The course is great, but need certain improvement for assignments and quizzes. The facts should be checked multiple times.

автор: Darshan S

31 дек. 2019 г.

Not enough real life examples throughout the video, makes it very hard to concentrate during the whole lecture.

автор: Mauricio A E G M

17 нояб. 2019 г.

This course is not useful to learn from scratch, but has some good things, for example the final assignment.

автор: Nikola G

14 янв. 2019 г.

Really didn't like the quiz parts of the course. If it was up to me I would do thorough revision of these.

автор: Chirag S

24 мая 2020 г.

The content was less informative and audio quality was poor. However, assignments are fun completing.

автор: Rohit S

21 мая 2020 г.

The online grader needs to be updated as there is constant error showing up though our code is right

автор: Gilad A

27 июня 2017 г.

The last assignment was super. apart for it, the assignments and the course were too easy

автор: Sai P

3 июня 2020 г.

There were a few corrections made during the videos which ended being quite confusing.

автор: Philip L

31 окт. 2017 г.

The assignments are extremely difficult, professor is a bit dry during lectures.

автор: Pakin S

10 янв. 2020 г.

How can i pass without reading discuss about problem with notebook

автор: Hao W

27 авг. 2017 г.

The homework is too easy to improve our understanding of ML