Probability for Machine Learning!

Here is all the probability theory you need for machine learning

MEDIUM

CHAPTERS
0:00 Linear Regression + Machine Learning
3:44 How Random Variables fit in
12:22 Maximum Likelihood + Probability Density Functions
16:18 Math derivation (with iid assumption, notation and more)

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[NicholasRenotte]

I just binged watched the whole Probability Theory playlist this morning! Smashed it Ajay!

[CodeEmporium]

Please checkout the accompanying blogpost in the description below. For more information on each topic discussed in the video (Random Variables, Probability mass / density functions), please refer to the "Probability Theory for Machine Learning" playlist.
Video Correction #1: Prices are dependent random variables that depend on number of bedrooms, age and sqft. So from 14:06 onwards, we should see the conditional distribution also depend on the X_ij terms. That said, the overall derivation should remain the same. Hope this helps!

[badriveera]

Great set of videos. One subtle point of clarification. If fy(yi) is a probability density function, then the value of fy(yi) for a particular house price would be zero since it is a continuous variable. How do you reconcile that? Appreciate your thoughts on this.

[carsten]

Hi, you're videos in this series have been so useful for my understanding, thank you!

Could I clarify something please? At 20:00, you say "in reality, all these PDFs can be assumed to be the same...practically meaning that probability that house #1 is $700-800 is the same for house #2 too, and all other houses".
I'm wondering whether this correct, my understanding: the PDFs are the same for every X value (gaussian) but they centre around a new mean for every value X value too. House number 1's X values mean that it will have a certain probability of being $700-800 according to the linear equation's y^ estimate at that X value. And house number 2's X value would follow that it will have a different probability of being $700-800 according the y^ estimate at that X value. Is this a correct interpretation?

Again, thank you so much for this series.

[theforthdoctor]

You forgot to mention the "Bambleweeny 57 Sub-Meson Brain" and the "atomic vector plotter".😉

[devharal]

Your videos are so accurate and intution behind learning via connecting concepts to machine learning is just awesome.

[chinmayeejoshi]

This is learning series has been excellent. Danke!

[keren]

I love your series. I wonder why there is (-1)x in the last derivation