M
5.0Reviewed Jun 22, 2020
Easy and engaging. But would loved it more if some more coding examples were given.
Machine Learning Algorithms: Supervised Learning Tip to Tail
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.
Status: Decision Tree Learning
Status: Performance Analysis
Course9 hours
Featured reviews
CW
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
VD
5.0Reviewed Aug 31, 2020
really good, wish it had covered random forest and decision trees and other supervised models as well.
MJ
5.0Reviewed Oct 29, 2019
Great course! I received so much useful information from AMII.
DK
5.0Reviewed Oct 3, 2020
Great learning..Talked almost all important issues.
SG
4.0Reviewed Apr 3, 2020
More maths to explain the underlying concepts will be good!!
DS
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.
FF
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!!!!!!
KG
5.0Reviewed May 9, 2020
The explanation of the topics are easy to understand due to the dynamics of theory, practical exercises and quizzes.
TH
5.0Reviewed 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.
EG
5.0Reviewed Jan 8, 2020
The whole specialization is extremely useful for people starting in ML. Highly recommended!
KS
5.0Reviewed Jun 13, 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