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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

Оценки: 399
Рецензии: 65

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


14 мая 2022 г.

This is an excellent course which goes into some depth on the different ML models and underlying complexity but it avoids getting bogged down into the details too much.


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.

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51–64 из 64 отзывов о курсе Machine Learning Algorithms: Supervised Learning Tip to Tail

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.

автор: Andrey Z

29 дек. 2021 г.

G​ood overview course. I hope authors will add more practice task in the futrure

автор: Kham H Y

28 окт. 2020 г.

Learn some valuable insights on scikit-learn capabitlity through the labs

автор: nouran a

7 мая 2020 г.

Many useful information but need some more explanation, overall awesome

автор: Saksham G

4 апр. 2020 г.

More maths to explain the underlying concepts will be good!!

автор: Daniel W

28 нояб. 2020 г.

Machine learning concepts are introduced well.

автор: Grecia P

3 мар. 2020 г.

week two was heavy

автор: MARRU R

18 дек. 2020 г.


автор: sandeep d

27 авг. 2020 г.


автор: Nicolas G

17 апр. 2021 г.

High level overview of Machine Learning, poor examples and incomplete labs.

автор: PIYUSH G

8 апр. 2020 г.


автор: Raghuram T

10 окт. 2020 г.

It could have been better if the trainer had included more hands-on examples rather than just tuning the slides and most important algorithms and its usage was there in the attached links for reading and I felt if this could have been taught in the training rather than just a document to read and self learn the quality of the course would have been fantastic.

автор: Sara K

30 сент. 2021 г.

The instructor is wonderful. She does not sound like a robot/reading off of cards like so many other instructors do. However, the practice assignments are still in draft form and missing files you need in order to complete them. That is why I gave this a one star.

автор: Carlos E

12 окт. 2021 г.

D​oesn't explain in a good way the models used. Only explain how to use them. I really need the math and a deep explanation to understand how to use the function. This is like they just give you the documentation and do examples.