Probability Theory - The Math of Intelligence #6

We'll build a Spam Detector using a machine learning model called a Naive Bayes Classifier! This is our first real dip into probability theory in the series; I'll talk about the types of probability, then we'll use Bayes Theorem to help us build our classifier.

Code for this video:

Hammad's Winning Code:

Kristian's Runner up Code:

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[RobbieCulkin]

It is so crazy how relevant these videos have been for me at work, where I'm just starting to do ML.
I needed to group my data in order to exclude outliers, and you had published the K-means clustering video the day before.
Next, I needed to decide if a CNN was the right approach to my problem, and the CNN video had been published that morning.
I then was told to look into using PCA for feature visualization and your Dimensionality Reduction video came out the next day.
It became clear that an RNN was the right solution for my next assignment and you posted that video 10 MINS BEFORE I STARTED SEARCHING!
Yesterday we were going deeper into theory and I was told to read up on frequentist vs bayesian probabilities. And now you post this.
This is absolutely insane, especially the timing. I'm a little freaked out by it, nice job Siraj.

[warrenarnold]

8:11 woaah😂 my neural networks cud not see that coming. i love ur vids, keep em coming

[vladislavdracula]

"Grabs your glocks when you see Lindley, call the cops when you see Lindley"

LOL!

[redrapunzel]

This is all I expect from a quality movie production: Great views, dramatic music, accurate scientific references and of course romance :D

[mrdbourke]

Awesome video Siraj! Was asked to try some probabilistic models at work and turned straight to your videos!

[RaymondWong]

I don't get the example given at 3:30
the info given are
probability of board breaks P(B)=0.3
probability of ride crash P(A)=0.5

at 3:51 suddenly P(B|A) P(A) = 1x0.06 ??
how do you get the values of P(B|A) and why P(A) suddenly becomes 0.06?

[kristianwichmann]

Thanks for the runner-up title. And nice work Hammad.

[BlockedIsNotAGoat]

that aesop rock instrumental caught me off guard!! thanks for the video, well explained!!

[jollyjokress]

Can't all teaching videos be like that? I don't need to switch between sources of entertainment/info to keep me attentive!

[manishadwani]

Hey hey hey I didn't see that coming.

I was watching you in front of my dad...

He

[nativealien]

I’m not even halfway through this video and I already love it. Don’t stop!

[jagriv]

Mr Siraj, you are a Master in the Art of probability theory, thank you for making it look simple

[hassanrevel]

oh this theorem has exploded my brain.

[andretorresdg]

Really good video and your way of teach

[aloominati]

Am I the only one who noticed that giant peanut on a roller coaster wearing a red cape?
Why don't I see anyone talking about it?

[teddyninan]

Hey Siraj (or anyone) - could you tell me what visualization tool you are using to create the animated cartoon. It starts around the first minute into the video describing the coin flip. Much appreciated!

[yashtawade]

"Do or Do Not. There is no Try." —Yoda.

[eligorniak]

Man, I loved this video, but the funny interruptions sometimes take off the focus and concentration😉.... but ok, I loved to learn more here ❤❤👏👏😊

[masteronepiece]

Thank you Sir. AJ for spreading love for Data Science .

[jasdishgill]

Hi Siraj, Thank you for sharing all this amazing information with us. I didn't know anything about programming, data science and all these mathematics but somehow I understand and implement 20 - 50 % of your work. Will get better with more practice.
Q. I am thinking about building a simple feed forward NN with few hidden layers for Intrusion detection using big datasets like KDD cup 99, NSL KDD (I know old but good to get started, I think not sure though). Please, provide me with some idea about how can I do this. As data set is large and contains symbolic features, I am not sure about can I use it. ? What's unique I can do in this? I am a super beginner, so in indeed of help. Thanks again.

Love your motivation, energy and style of course.