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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: 7,024
•
Рецензии: 1,278

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.

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:

1126–1150 из 1,257 отзывов о курсе Applied Machine Learning in Python

автор: KOSHAL K

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1 мар. 2020 г.

Its a very good course for an intermediate level.

автор: Vinay P d L R

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26 сент. 2017 г.

goes too fast and too shallow to deserve 5 stars

автор: Anendra G

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30 апр. 2018 г.

Awesome theory about machine learning concepts.

автор: harsh a

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3 февр. 2018 г.

Good course.

Thanks to entire team

Harsh Arora.

автор: XJTLU

•
19 июня 2019 г.

Some concepts should be introduced in detail.

автор: Amita D

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18 мая 2018 г.

Need more information about more algorithms

автор: Ruben W

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8 сент. 2019 г.

Best course so far in this specialisation

автор: Alan F

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28 февр. 2018 г.

Good course but there's a lot of material

автор: Abdulwaheed M

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17 июня 2020 г.

Teaching is very good and it is helpfull

автор: Alperen B O

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16 дек. 2020 г.

I get late feedback for lab assignments

автор: Ramya K

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15 июля 2019 г.

Well-organized but assignments too easy

автор: Supratim D

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10 авг. 2017 г.

Very informative but bit too difficult.

автор: ROHIT J

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2 авг. 2020 г.

very helpfull.thanks for creating this

автор: Xiang C

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12 мая 2020 г.

It's good to learn how to use sklearn.

автор: Jagadish C A

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19 сент. 2019 г.

Gives good overview of ML using Pyton

автор: Shreekant G

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17 июля 2019 г.

Really taught best ML algorithms

автор: xingkong

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9 авг. 2017 г.

quiz is harder than assignment.

автор: shreyash t

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28 июля 2020 г.

overalll good way to start ml

автор: Vaibhav S

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27 мая 2020 г.

way better than last teacher.

автор: Nicolas B

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5 июля 2017 г.

Muy buen curso, muy completo.

автор: 李祥泰

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15 авг. 2017 г.

Nice courses with nice quiz!

автор: 刘倬瑞

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29 июля 2017 г.

Useful, though a little easy

автор: Landon M L

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9 июля 2017 г.

the discussion forum is good

автор: Rizvaan M

•
21 мая 2020 г.

This was a great course.

автор: Yassin B M

•
17 июля 2020 г.

Good Course , Thanks!!

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