Learner Reviews & Feedback for AI Workflow: Machine Learning, Visual Recognition and NLP by IBM
About the Course
courses in order as they are not individual independent courses, but part of a workflow where each course builds on the previous ones. Course
4 covers the next stage of the workflow, setting up models and their associated data pipelines for a hypothetical streaming media company.
The first topic covers the complex topic of evaluation metrics, where you will learn best practices for a number of different metrics including
regression metrics, classification metrics, and multi-class metrics, which you will use to select the best model for your busine...
Top reviews
NM
Jul 7, 2020
Dear Team ,Namaste Everyone !! Excellent Course structure - ML, VR and NLP.Great Learning Module Design by All Faculty. Thanks to everyone!!!
PL
May 3, 2020
The teaching materials are well presented and clear.Just that the level of this course is a bit not advanced enough.
1 - 14 of 14 Reviews for AI Workflow: Machine Learning, Visual Recognition and NLP
By Neela M
•Jul 7, 2020
Dear Team ,
Namaste Everyone !!
Excellent Course structure - ML, VR and NLP.
Great Learning Module Design by All Faculty.
Thanks to everyone!!!
By Patrick L
•May 3, 2020
The teaching materials are well presented and clear.
Just that the level of this course is a bit not advanced enough.
By vignaux
•Nov 17, 2019
Great course with a lot of practice and smart meaning !
By Julio C
•Jul 30, 2020
Great training !!!
By Suryabrata D
•Jul 6, 2020
very Informative
By Takahide M
•Jan 4, 2023
Very Nice.
By AKARAPU K
•Aug 12, 2024
Very good
By Холмухамедова З Б к
•May 31, 2022
Perfect
By Huỳnh T Â
•Mar 15, 2025
hay
By BHAVANA g
•Sep 22, 2020
Its pretty difficult to follow up with this course. We must have a good knowledge on Neural n/ws prior starting this course.
By S M A J
•May 28, 2020
Good for using IBM tools
By Dennis L
•Aug 29, 2020
Theory Overview only
By David L
•Aug 26, 2020
Aspects of this course could be worked on with regards to smoothness, conceptual teaching and grammatical/spelling errors.
Much of the course had confusing terminology/grammatical forms which made multiple lessons difficult to understand. The video quality was, for the most part, very well done -- but some videos moved too quickly to follow (although it may just be my current level is too low).
I really enjoyed the case studies for the most part; they were challenging and informative, forcing you to learn yourself. There were a couple of areas where I would've appreciated more guidance, such as setting up the MLP/CNN at the end of Week 2. I had no idea that we needed to use a sparse-categorical-crossentropy loss function until I looked at the solution -- and I'm not sure other students would know the same.
Otherwise, it was a useful course.
By Quinzy F
•Oct 10, 2024
Too much time on Theory. Little practical work. The practical work is basic and not advanced. Misspellings in Material and Downloads indicate a poor attention to detail from instructors.