D-Lab's 6 hour introduction to machine learning in Python. Learn how to perform classification, regression, clustering, and do model selection using scikit-learn in Python.
Here is some feedback for the workshop and I'd love to participate in improving the workshop!
Presenter:
Excellent pacing and very clear explanation of complex concepts!
Multiple breaks help pacing the workshop and participants used that time to ask questions.
I like how presenter showed what the future workshops on deep learning will cover!
Content:
This workshop covers a lot of important content and concepts! Participants expressed that the workshop was very useful (in the chat at the end of the workshop)
The instructor used iPad to draw diagrams / explain and incorporating by imbedding photos into the jupyter notebook will be helpful.
Introduction slide decks explaining broad concepts machine learning and what we will cover (and not cover) were very helpful.
Some repetition of the process might be helpful (explicitly showing case by case what the model, the loss function, the goal etc). There are a lot of jargon and it might be helpful to have a cheat sheet / list of terms with brief definition might also be helpful.
Bullet points for objectives for each lesson might be helpful to clarify what concepts are covered for each lesson!
It would be nice to have a "take-home message slide" that summarizes what we have covered for the workshop.
There are challenge questions with no coding component (For example, lesson 03, challenge 1), and it would be a good opportunity to use zoom poll function to see where participants are in terms of understanding concepts.
Here is some feedback for the workshop and I'd love to participate in improving the workshop!
Presenter:
Content: