Name: Machine Learning Algorithms: Supervised Learning Tip to Tail
Brand: Alberta Machine Intelligence Institute
Availability: OnlineOnly
Rating: 4.7 (66 reviews)

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4.7417 reviews

About this 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.

Featured reviews

4.0Reviewed May 6, 2020

Many useful information but need some more explanation, overall awesome

5.0Reviewed Sep 29, 2020

Great course, easy to grasp the main idea of how to assess and tune the performance of question-answering machines learned by machine learning algorithms through data

5.0Reviewed Aug 31, 2020

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

4.0Reviewed Oct 27, 2020

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

5.0Reviewed Dec 8, 2020

I found the course to be enough detailed to get clarity on the basic concepts of Supervised learning algorithms. I hope to apply the learning from the course in work!

5.0Reviewed Oct 29, 2019

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

5.0Reviewed Apr 16, 2020

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

5.0Reviewed Jun 4, 2020

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

5.0Reviewed 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.

5.0Reviewed May 9, 2020

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

5.0Reviewed May 1, 2020

A good refresher on some commonly found learning algorithms.

5.0Reviewed May 6, 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.