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I'm working through this chapter in AI4I:

https://makerspace.aisingapore.org/courses/ai-for-industry/lessons/eda-2-preprocessing-for-machine-learning-in-python-2/

And I got up to this video, in which the instructor says "you should already be familiar with this", at timestamp 00:31.

https://campus.datacamp.com/courses/preprocessing-for-machine-learning-in-python/introduction-to-data-preprocessing?ex=7

... which I have yet to encounter, so I check the main course page on DataCamp:

https://learn.datacamp.com/courses/preprocessing-for-machine-learning-in-python

And in the prerequisites section we have this:

image

So DataCamp is quite clear that "supervised learning with scikit learn"  is indeed a prerequisite.

So now I take a look at the course progression on AI4I, and I see this:

AI4I-3: Exploratory Data Analysis
 
EDA-1: Introduction to Data Visualisation in Python
EDA-2: Preprocessing for Machine Learning in Python
EDA-3: Feature Engineering for Machine Learning in Python
EDA-4: Feature Engineering for NLP in Python

1 Quiz

AI4I-3-Q: Exploratory Data Analysis Quiz
 
AI4I-4: Statistical Thinking
STAT-1: Statistical Thinking in Python (Part 1)
STAT-2: Statistical Thinking in Python (Part 2)
STAT-3: Dimensionality Reduction in Python

1 Quiz

AI4I-4-Q: Statistical Thinking Quiz
 
AI4I-5: Supervised Learning
SUP-1: Setting up your Machine Learning Environment
SUP-2: Supervised Learning with scikit-learn
SUP-3: Regression
 
image

... so AI4I is indeed aware of this course, but stipulates participants to start this in 8 courses after Preprocessing for Machine Learning in Python, however DataCamp says this this course should be done before Preprocessing for Machine Learning in Python.

Which one is it? Which course should I do before the other?

 
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2 Answers
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Hi @bguiz. Thank you for your feedback.

We have reviewed the content and feel that the AI4I course progression is suitable. However, we do note that it contradicts with the stated prerequisite on DataCamp.

We will review and consider rearranging the modules to match DataCamp.

@siowy Perhaps if you think the flow does indeed work, just mention that on this page:
> https://makerspace.aisingapore.org/courses/ai-for-industry/lessons/eda-2-preprocessing-for-machine-learning-in-python-2/ < I might suggest also including link to the relevant scikit learn docs which would be helpful to read in lieu of completing the entire datacamp module. For example, in this particular case, a link to https://scikit-learn.org/stable/modules/generated/sklearn.model_selection.train_test_split.html

would have been helpful.

Thank you for the feedback. It is helpful. We are looking to implement a change for this soon.

@siowy Just completed this particular course on DataCamp, I was able to get through it without getting stuck, but resorted to a lot of trawling through docs to figure things out. The NB and Tf/Idf sections in particular required a lot of background.

All said, my suggestion is that you either need to rearrange the course order to follow the DataCamp pre-requisites order, or you add your own AI4I content that covers the minimal relevant parts before this chapter.

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@bguiz
Got it. Thanks for the detailed feedback! The course will be made better from your input.

and going one more step ahead, it looks like the course after that, https://makerspace.aisingapore.org/courses/ai-for-industry/lessons/eda-4-feature-engineering-for-nlp-in-python-2/ looks like it has the same issue plus one more, as the linked datacamp course for this requires not only https://www.datacamp.com/courses/supervised-learning-with-scikit-learn as a pre-requisite, but https://www.datacamp.com/courses/introduction-to-natural-language-processing-in-python as well.

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