Retour à Machine Learning Algorithms: Supervised Learning Tip to Tail

4.7

étoiles

80 évaluations

•

18 avis

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....

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.

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.

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par 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)

par 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.

par 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.

par 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.

par 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!!!!!!

par 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.

par Emilija G

•Jan 09, 2020

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

par Valerii M

•Mar 31, 2020

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

par M J

•Oct 30, 2019

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

par Miguel A S M

•Oct 15, 2019

Excellent.

Teach you practical stuff that other courses don't.

par Hamza M

•May 02, 2020

A good refresher on some commonly found learning algorithms.

par Cheng H Z

•Oct 10, 2019

Explained things clearly

par Nouran G

•May 07, 2020

Many useful information but need some more explanation, overall awesome

par Saksham G

•Apr 04, 2020

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

par Grecia P

•Mar 03, 2020

week two was heavy

par 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.

par BINSHUANG L

•Dec 15, 2019

Good coverage of the topics in supervised learning. However, lacks depth in some of the concepts.

par PIYUSH G

•Apr 08, 2020

good