Chevron Left
Вернуться к Machine Learning Algorithms: Supervised Learning Tip to Tail

Отзывы учащихся о курсе Machine Learning Algorithms: Supervised Learning Tip to Tail от партнера Alberta Machine Intelligence Institute

4.7
звезд
Оценки: 391
Рецензии: 64

О курсе

This course takes you from understanding the fundamentals of a machine learning project. Learners will understand and implement supervised learning techniques on real case studies to analyze business case scenarios where decision trees, k-nearest neighbours and support vector machines are optimally used. Learners will also gain skills to contrast the practical consequences of different data preparation steps and describe common production issues in applied ML. To be successful, you should have at least beginner-level background in Python programming (e.g., be able to read and code trace existing code, be comfortable with conditionals, loops, variables, lists, dictionaries and arrays). You should have a basic understanding of linear algebra (vector notation) and statistics (probability distributions and mean/median/mode). This is the second course of the Applied Machine Learning Specialization brought to you by Coursera and the Alberta Machine Intelligence Institute....

Лучшие рецензии

SK
11 апр. 2020 г.

Excellent course. In which I had in-depth knowledge of all algorithms and the way she explained attracts to listen except for her spontaneity and speed in progressing.

DS
6 мая 2020 г.

Excellent course for an overview of different ML algorithms. The course is made from a perspective of giving insights in process and not too many mathematical details.

Фильтр по:

26–50 из 63 отзывов о курсе Machine Learning Algorithms: Supervised Learning Tip to Tail

автор: David B T

3 апр. 2021 г.

Good introduction for applying ML at a high level.

автор: SATHEESH K G

28 июня 2020 г.

Good content and nicely delivered!

автор: Saulo A G S

29 окт. 2020 г.

I learn many new concepts

автор: Cheng H Z

10 окт. 2019 г.

Explained things clearly

автор: KANALA J

7 дек. 2020 г.

Excellent Teaching!:)

автор: Rimmon S B

1 окт. 2020 г.

Really cool teaching!

автор: UPPUNURU K R

8 дек. 2020 г.

Great expilination

автор: KOTA V

6 дек. 2020 г.

good for learning

автор: Jorge M R V

14 мар. 2021 г.

excellent course

автор: AVASARALA S

7 дек. 2020 г.

Learnedly well

автор: D V R

23 дек. 2020 г.

Great Course

автор: Danilo C D C J

17 сент. 2020 г.

Nice course!

автор: RUCHITHA S K

6 сент. 2021 г.

It was good

автор: kaki m p

16 дек. 2020 г.

good course

автор: 221810304033 N V V

5 авг. 2021 г.

too good

автор: KONDAPALLI D

11 нояб. 2020 г.

great!

автор: 121710317007 C J

12 дек. 2020 г.

good

автор: MATTHURTHI P V D R

12 дек. 2020 г.

good

автор: Harika B L

9 дек. 2020 г.

good

автор: 121710308009 B G

9 дек. 2020 г.

good

автор: KANDULA J C

21 нояб. 2020 г.

good

автор: VUPPUTURI R K

28 окт. 2020 г.

Good

автор: CHILUKURU S A

22 окт. 2020 г.

nice

автор: Ubeydullah K

11 нояб. 2021 г.

I am grateful to have attended this course. I have learned quite a lot and I believe, now, I have a solid general understanding of some of the most common ML algorithms. The instructor, Anna Koop is very knowledable and she has a very clear way of explaining the concepts. The reason why I haven't rated it 5-star is because the course is not really designed around practice. It is rather conventional in the sense that the instructor takes the central stage and students don't get to practice much. Quizzes are good, but they are far from being enough to give learners actual experience of "doing" machine learning. I am aware of the vastness of the field, and maybe that's why they kept the course intructor centered, but I still expect courses to push learners more in trying out the methods themselves and learning by doing. Still, the course is very beneficial and has valuable content. It is a great knowledge source as well. Warmest regards.

автор: Morgan S

23 мая 2021 г.

This course is a great overview of ML concepts. The professor is superb! I did not give 5 stars because the labs need to be improved. The labs are too simple. This course should provide more opportunities for applying the ML concepts.