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Learner Reviews & Feedback for Machine Learning Algorithms: Supervised Learning Tip to Tail by Alberta Machine Intelligence Institute

4.7
stars
410 ratings

About the Course

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....

Top reviews

TH

May 14, 2022

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.

SK

Apr 11, 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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51 - 65 of 65 Reviews for Machine Learning Algorithms: Supervised Learning Tip to Tail

By CHILUKURU S A

•

Oct 22, 2020

nice

By Ubeydullah K

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Nov 11, 2021

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.

By Morgan S

•

May 23, 2021

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.

By Andrey Z

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Dec 29, 2021

Good overview course. I hope authors will add more practice task in the futrure

By Kham H Y

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Oct 28, 2020

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

By Nouran A

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May 7, 2020

Many useful information but need some more explanation, overall awesome

By Saksham G

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Apr 4, 2020

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

By Daniel W

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Nov 28, 2020

Machine learning concepts are introduced well.

By Grecia P

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Mar 3, 2020

week two was heavy

By MARRU R

•

Dec 18, 2020

nice

By sandeep d

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Aug 27, 2020

nice

By Nicolas G

•

Apr 17, 2021

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

By PIYUSH G

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Apr 8, 2020

good

By Raghuram T

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Oct 10, 2020

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.

By Carlos E

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Oct 12, 2021

Doesn'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.