Speed Learning Machine Learning concepts in 1 Hour

If I only had one hour to familiarize myself with an unknown machine learning subject, I would try to choose some steps from the following list (ranked by priority):

1. Find a short YouTube video explaining the basics of the subject, I usually watch them at 1.5x speed.
2. Ask ChatGPT for a summary.
3. Look for a Colab notebook. On Google, the magic trick is to follow your query with the Colab address prefix, for example: “segmentation model site:colab.research.google.com”. The big advantage is that there is no time lost setting up the environment and the explanations are often detailed.
4. Read experience feedback on Reddit, LinkedIn or Twitter. It is fairly easy, even on LinkedIn, to find relevant posts with the right keywords.
5. Check if there is a relevant model on Hugging Face. The description pages are often rich in information: usage examples, training methodology, datasets used...
6. Take a Udemy course, they are often on sale for 10-15€. I think it's fair to pay that amount if it saves me a lot of time. I watch the parts that interest me at 1.5x speed.
7. Do my own tests and experiments.
8. Read specialized blog articles (Medium, Towards Data Science...).
9. Find a GitHub project and analyze the implementation.
10. Consult scientific publications on the subject.

For purists, it may seem counterintuitive that scientific publications come last. The pragmatic stance here is to emphasize learning by doing. It often allows for faster and more motivating skill building. In a second step, doing usually naturally leads me to read publications, attracted by the curiosity to understand how it works in detail. I also find it simpler to read them after having practiced the subject beforehand.

The other principle is to capitalize on what your peers have already done before you and not to reinvent the wheel.