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Вернуться к Практическое компьютерное обучение

Отзывы учащихся о курсе Практическое компьютерное обучение от партнера Университет Джонса Хопкинса

Оценки: 3,197
Рецензии: 615

О курсе

One of the most common tasks performed by data scientists and data analysts are prediction and machine learning. This course will cover the basic components of building and applying prediction functions with an emphasis on practical applications. The course will provide basic grounding in concepts such as training and tests sets, overfitting, and error rates. The course will also introduce a range of model based and algorithmic machine learning methods including regression, classification trees, Naive Bayes, and random forests. The course will cover the complete process of building prediction functions including data collection, feature creation, algorithms, and evaluation....

Лучшие рецензии


16 янв. 2017 г.

excellent course. Be prepared to learn a lot if you work hard and don't give up if you think it is hard, just continue thinking, and interact with other students and tutors + Google and Stackoverflow!


13 авг. 2020 г.

recommended for all the 21st centuary students who might be intrested to play with data in future or some kind of work related to make predictions systemically must have good knowledge of this course

Фильтр по:

601–606 из 606 отзывов о курсе Практическое компьютерное обучение

автор: Abhilash R N

4 дек. 2019 г.

This course is NOT for the beginner. Take time to finish all the beginner and foundation courses and then take time to learn R

автор: Yesica B

29 дек. 2021 г.

I wanna know, what is happening with my grade with this course. I still wait long time ago. Please, help me.

автор: Emily S A

25 мая 2020 г.

In my opiion, this course needs to be improved a lot. There are almost nothing Practical Machine Learning.

автор: yi s

19 июля 2016 г.

too general no depth, not recommended for science or engineering degree holders

автор: Stephen E

27 июня 2016 г.

To be honest I don't think this is worth the money.

автор: Stephane T

31 янв. 2016 г.

Too much surface, not enough depth.