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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: 7,151
Рецензии: 1,298

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!!

Фильтр по:

1076–1100 из 1,277 отзывов о курсе Applied Machine Learning in Python

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

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

автор: ZHAI L

11 мая 2018 г.

Compared to previous two courses in this specialization, this course need more time for self-learning.

автор: Justin M

11 апр. 2018 г.

Great course overall. Only reason for 4 stars is some of the assignments could use a bit more clarity.

автор: Manjeet K

14 сент. 2019 г.

Easy to learn the course, just be focussed. Its an applied ML course, not to expect any mathematics.

автор: Ulka K

27 февр. 2020 г.

I found the dataset in the last assignment difficult to interprit. I was hoping for a simpler one.

автор: Stephen R

8 мая 2018 г.

Wish there were a little more theory, realize it's an "Applied" course but still seemed lacking

автор: Michel H

23 янв. 2020 г.

helpfull, but so many information in little time. Difficult to get clarified the ideas behind

автор: Samantha

5 апр. 2020 г.

Very great courses ! It helps to deepen my knowledge in Machine learning. Very recommend it!

автор: Koffi M K

14 окт. 2019 г.

A part from some small issues when doing the last assignment(4), Everything was all right.

автор: Nicholas P

29 дек. 2020 г.

Good content and teacher but needs more interactivity before the final project every week

автор: WhiteCR

15 февр. 2020 г.

Good course for practicing machine learning algorithms with Python Sci-kit Learn package.