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Вернуться к Design Thinking and Predictive Analytics for Data Products

Отзывы учащихся о курсе Design Thinking and Predictive Analytics for Data Products от партнера Калифорнийский университет в Сан-Диего

4.5
звезд
Оценки: 61
Рецензии: 11

О курсе

This is the second course in the four-course specialization Python Data Products for Predictive Analytics, building on the data processing covered in Course 1 and introducing the basics of designing predictive models in Python. In this course, you will understand the fundamental concepts of statistical learning and learn various methods of building predictive models. At each step in the specialization, you will gain hands-on experience in data manipulation and building your skills, eventually culminating in a capstone project encompassing all the concepts taught in the specialization....
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1–11 из 11 отзывов о курсе Design Thinking and Predictive Analytics for Data Products

автор: Pratik P

10 июля 2019 г.

This course takes you from learning to do many data analytics and Machine learning tasks manually to all the way doing it much more efficiently using the standard libraries. Overall, a great course to give you a rock solid foundation in this field.

автор: ANUSHREE C

26 мар. 2021 г.

Excellent course.

автор: Yassine E

21 февр. 2020 г.

Awesome

автор: ASHUTOSH S

8 мар. 2021 г.

nice

автор: Clarence E Y

3 янв. 2020 г.

This course provides practical techniques used for regression and classification of datasets. These techniques are important to gain understanding and experience in building a data pipeline in the design process. Logistic Regression, Support Vector Machines, and K-Means approaches are covered along with Jaccard, F-1 error evaluation and Gradient Descent.

автор: Anshu P M

8 мая 2021 г.

It was great course ,helped me in getting better understanding of data and do predictive modeling.

автор: Reinhold L

21 июня 2019 г.

Very informative course and very good documentation as well as practical examples.

автор: Nguyen T

13 июня 2020 г.

While the instructor does appear to be very knowledgeable, many mathematic concepts are brought up during this course that are not always followed up with implementation in Python. For instance, there is a demo in Python for Linear Regression and Autoregression, but some brought up methods are not demonstrated in Python. It is a shame, though, because this course had a lot of promise.

автор: Sebastian R B

20 февр. 2021 г.

Good introduction to the concepts of machine learning: Linear Regression and Classification (Logistic Regression)); however, not good emphasis was made to the application nor code.

автор: Surendar R

14 июня 2019 г.

Course contents are very good, able to learn a lot.

However, very frustrating system is - project assignment submissions of last week has to wait for infinite time to be graded by peers. Wait time to get feedback on your submission is extremely long and very annoying to have such a long wait.

Either, mentors of this course should step forward and help in this review process at periodic intervals or, this system should go away and it should NOT be mandatory requirement to complete this course.

For poor grading system that is in place for project submission - am submitting 2 stars, otherwise I would have gone for 4 or 5 hands down

автор: Olugbenga O A

18 янв. 2020 г.

The Technical parts felt too rushed.