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Вернуться к Практическое компьютерное обучение

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

Оценки: 2,667
Рецензии: 500

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


Jan 17, 2017

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!


Mar 01, 2017

Issues of every stage of the construction of learning machine model, as well as issues with several different machine learning methods are well and in fine yet very understandable detail explained.

Фильтр по:

226–250 из 491 отзывов о курсе Практическое компьютерное обучение

автор: Simeon E

Aug 02, 2017

Great Course. No so easy, as I expected, but, definitely, it worth all the time I've spent on it. Be careful: it requires a lot of self-studying and don't forget to read the Course Forum.

автор: Fernando M

Sep 04, 2017

Great material. Really enjoyed it


Oct 30, 2017

Good Approach......Valuable Course......!!!

автор: Matthew W

Mar 01, 2016

High level and brief overview but found it informative introduction into machine learning with R. The final project is fun and interesting.

автор: Rui R

Feb 06, 2017

One of the best courses in the Data Science Specialization,

автор: Kevin W

Feb 11, 2017

Great course

автор: Yi-Yang L

May 19, 2017


автор: antonio q

Feb 27, 2018

it was great, simply though exhaustive, thanks a lot

автор: David Y

Feb 09, 2016

Enjoyed without reservation

автор: FARZAD R

Jun 16, 2017

Really wonderful and very practical course .

автор: Peter T

Feb 29, 2016

Love it.

автор: Triston C

May 27, 2017

This course really demystified machine learning, and provided practical steps and guidance on how to create predictive models. While I do wish there were more resources on how to tune models and investigate specific model parameters, I understand that there just wasn't enough time. I couldn't imagine a better course for a solid foundation in this skill.

автор: Vinicio D S

May 22, 2018

You will learn how to use the caret package and learn how to implement ML algorithms. If you want the theory behind it, you need to go to other courses

автор: YOGESH C

Jun 18, 2016

great course

автор: Rodrigo C

Oct 14, 2017

Great course.

автор: Artem A

Apr 14, 2016


автор: Tasif A

Jun 16, 2017

Great Course. Must do it.

автор: HIN-WENG W

Feb 07, 2017

PML is a deep subject and this course is an excellent foundation for further studies. Prof Leek has taught brilliantly on the basic concepts of PML given the short time of 4 weeks. You need college level statistics to fully appreciate the theories of the PML's lectures.

автор: Philippine R

May 22, 2017

I learned so much in such a short period of time. Challenging, very hands on, great theoretical foundations!

автор: Mertz

Mar 20, 2018

Real practical machine learning!

автор: Albert C G

Sep 04, 2016

Fun course, also practical and useful

автор: yefu w

May 30, 2017

Great Course!

автор: Sarah S

May 31, 2017

I enjoyed detailed information and was very straight forward to understand.

автор: Rishabh J

Aug 22, 2017

All the major machine learning algorithms and techniques are provided in a way that you can begin using them right away. The course project also provides an opportunity to apply the different techniques learnt in class to a rather messy dataset.

автор: Yong-Meng G

Jun 20, 2017

Insightful and practical ! One of the best so far.