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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

4.7
звезд
Оценки: 166
Рецензии: 28

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

DS

May 07, 2020

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.

SK

Apr 12, 2020

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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1–25 из 28 отзывов о курсе Machine Learning Algorithms: Supervised Learning Tip to Tail

автор: Luiz C

Sep 11, 2019

Had higher expectations. Concepts not well and clearly explained. Notebooks bugged (we are actually warned about it), but even so not so interesting. Plan of the Course not so rational: why include the one section about model parameters on its own, rather than for each model.

I give it a 3 as the Instructor is smily and engaging, but it's a 2.5 mark (I have done another ML MOOC on another concurrent platform about the same topic, and the quality was much higher)

автор: efren c

Jan 13, 2020

Excellent course, I was looking for a course which didn't explore advance math or go into the specifics of a particular ML method but which focuses on the main differences among then and teach about the whole process of M, this is the best course for that.

автор: S. k

Apr 12, 2020

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.

автор: Dishant S

May 07, 2020

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.

автор: Alvaro V

Jul 12, 2020

Very important concepts about supervised ML are presented. Really liked the course but a little stressed about the graded quices, even though I enjoyed very much.

автор: Fahim F

Apr 17, 2020

Great course but less in-depth knowledge about each of the hyper parameters and under the hood view of Algorithms.But excellent. Thanks!!!!!!

автор: Sornamuhilan S P

Jun 19, 2020

A great short capsule course to get overall bird view on Supervised learning. Much needed one for both practitioners and new beginners.

автор: KAZI S S

Jun 14, 2020

although the course felt a little hurried, I found the course and the instructor to be very engaging. I look forward to learning more

автор: Bishrul H

Jun 05, 2020

It's a nice course for those who likes to learn the supervised machine learning algorithms with practical experience.

автор: Kevin A D G

May 10, 2020

The explanation of the topics are easy to understand due to the dynamics of theory, practical exercises and quizzes.

автор: Vinayak D

Sep 01, 2020

really good, wish it had covered random forest and decision trees and other supervised models as well.

автор: Emilija G

Jan 09, 2020

The whole specialization is extremely useful for people starting in ML. Highly recommended!

автор: Munem

Jun 23, 2020

Easy and engaging. But would loved it more if some more coding examples were given.

автор: Valerii M

Mar 31, 2020

Nice course! Good idea to add more practice with Jupyter Notebooks!

автор: M J

Oct 30, 2019

Great course! I received so much useful information from AMII.

автор: Miguel A S M

Oct 15, 2019

Excellent.

Teach you practical stuff that other courses don't.

автор: Hamza M

May 02, 2020

A good refresher on some commonly found learning algorithms.

автор: SATHEESH K G

Jun 28, 2020

Good content and nicely delivered!

автор: Cheng H Z

Oct 10, 2019

Explained things clearly

автор: Danilo C D C J

Sep 17, 2020

Nice course!

автор: nouran a

May 07, 2020

Many useful information but need some more explanation, overall awesome

автор: Saksham G

Apr 04, 2020

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

автор: Grecia P

Mar 03, 2020

week two was heavy

автор: sandeep d

Aug 27, 2020

nice

автор: Enyang W

Feb 21, 2020

This course covers lots of important ideas and knowledges for Machine Learning practitioners. It is definitely nice to deal with topics such as grid search or scikit-learn, but I think the course only covers these topics in a nutshell, it is more superficially discussed. If you are interested in Machine Learning, you should definitely bring your own motivation to dive deeper into those topics.. Also, Dr. Koop speaks very very fast though.. I attended courses by Andrew Ng, his courses provide a way better comprehensibility for listeners. The notebooks are a bit weird, very easy to understand and are hence not challenging. If you really want to understand the algorithms deeply, I don't think this course is the right one. But all in all, I completed the course, but I don't think I was able to understand everything by taking the course only.