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.
M
Easy and engaging. But would loved it more if some more coding examples were given.
CW
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
really good, wish it had covered random forest and decision trees and other supervised models as well.
EG
The whole specialization is extremely useful for people starting in ML. Highly recommended!
DK
Great learning..Talked almost all important issues.
RM
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!
KS
although the course felt a little hurried, I found the course and the instructor to be very engaging. I look forward to learning more
DS
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.
BH
It's a nice course for those who likes to learn the supervised machine learning algorithms with practical experience.
BS
Excellent instruction. One of the best in ML. Could use a bit more python though.
SK
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.
FF
Great course but less in-depth knowledge about each of the hyper parameters and under the hood view of Algorithms.But excellent. Thanks!!!!!!
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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.
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.
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.
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.
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!
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
Great course but less in-depth knowledge about each of the hyper parameters and under the hood view of Algorithms.But excellent. Thanks!!!!!!
A great short capsule course to get overall bird view on Supervised learning. Much needed one for both practitioners and new beginners.
although the course felt a little hurried, I found the course and the instructor to be very engaging. I look forward to learning more
It's a nice course for those who likes to learn the supervised machine learning algorithms with practical experience.
The explanation of the topics are easy to understand due to the dynamics of theory, practical exercises and quizzes.
really good, wish it had covered random forest and decision trees and other supervised models as well.
The whole specialization is extremely useful for people starting in ML. Highly recommended!
Easy and engaging. But would loved it more if some more coding examples were given.
Nice course! Good idea to add more practice with Jupyter Notebooks!
Great course! I received so much useful information from AMII.
Excellent.
Teach you practical stuff that other courses don't.
A good refresher on some commonly found learning algorithms.
It was an excellent course. Thank you! You are a master!
Great learning..Talked almost all important issues.