A Critical Skill People Learn Too LATE: Learning Curves In Machine Learning.

An introduction to two fundamental concepts in machine learning through the lens of learning curves. Overfitting and Underfitting.

📚 My 3 favorite Machine Learning books:

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[underfitted]

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[kraeftigerkanacke]

I have read a lot of articles and watched a few tutorials. But THIS is the perfect explanation for beginners in the ML field. Thank you very much.

[orbitinggeek]

What an amazing explanation ! It shows how well you yourself understood it - so glad I found you on Twitter !!

[mgreek]

Straigtht to the point. I honestly like how you talk more of theory and analysis rather than code.

[Singasongwithme]

Teaching style is too unique and too good 👍

[jacemi]

Excellent analogy 👏 Thank you very much Santiago!! Your videos are so cool and to the point !!

[agenticmark]

That was easily the best explanation of learning curves. I have seen each of those, except the perfect curve, but I will keep trying!

[VelazquezJFP]

I belive you have thought so many artificial brains that you know how to get information in the most slow human brains out there. You repeat the fundamentals with a different tone, get letters on screen and give it time to absorve ensuring there is no overfit or underfit in my learning today.

Great job and you got a new subscriber.

[JordiRosell]

Thanks for this video. I would say validation set, cross-validation sets or resamples instead of testing. But the main ideas are the same. I only use the holdout set once for the last fit and some people can misinterpret some concepts here.

[paulallen]

Superb explanation of the what is the problem and how to approach solving it.

[tehsupervik]

Great quality of information and really precise. So helpful for a beginner like me

[kozaTG]

if you can't explain it to a 6 year old, you don't understand it yourself. brilliant video

[rewiredbyadhd]

This is one of the best Machine Learning channels I've seen. Thanks Santiago 🙏🏻 You have a new subscriber. I came here from Twitter, your content there is super good❤️🙏🏻

Please keep making explanatory videos with simple language, so anyone can understand.
Thank you again🙏🏻

[Fantalic]

this is so good. so much important information in short time. big thanks. :)

[mohammadmassri]

Best explanation ever on YouTube! Keep it up man!

[muzammilrizvi]

Can't describe how helpful and beautiful this video is, simply Amazing.

[msfasha]

Great explanation with lots of illustrations, simply a very good job, keep going.

[atakanbilgili]

Great explanations, especially useful when you work on your own dataset rather than the Kaggle ones.

[ScottSavageTechnoScavenger]

YES!!!! You just solved a problem I ran into years ago!

[burhanrashidhussein]

Qn. Can we use the concept of overfiting to understand how good is our training data? Eg. What if we can not overfit our training data? Can we say our data is not good enough to train the model?