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Overall this lesson seemed very advanced for novice learners, the concept of a model/features/labels was difficult to convey in a short time in a clear, concise manner and would benefit from more visual graphics to help explain some of the core concepts.
Recommendations:
Rearrange (switch) the order of Chapter 3 and Chapter 4. An audience with an introductory level of experience may benefit from first learning what AI is good at before learning how it works.
Chapter 3: One of the key sections of the lesson is the introduction to modeling & algorithms, though the content on feature engineering and embedding seemed like it is not an essential concept for beginners to AI and could be de-emphasized or added as an addendum.
Thank you @leighphan , Daniel and I talk about this during the sprint as well. We have always wanted to make this lesson about concepts which is why putting the terminology early in the lesson seemed like a good idea. I have recent experience of giving a talk around similar material to a non-technical audience. All of the preceding talks used all of this terminology with little explanation, and then I went a little further into the detail later in the day. What it meant was that they'd heard the terminology used in context first and could then learn more about it, and this order seemed to work well as the more technical look was less abstract for the audience.
So I think what we decided is to move this to the end as potentially an optional module. However, this means we need to be careful of our use of terminology in the rest of the episodes as they won't have already been introduced.
Feedback for Chapter 03 – Machine Learning Modelling Concepts
Overall this lesson seemed very advanced for novice learners, the concept of a model/features/labels was difficult to convey in a short time in a clear, concise manner and would benefit from more visual graphics to help explain some of the core concepts.
Recommendations:
Originally posted by @leighphan in #82 (comment)
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