As a senior data science student, I want to enter the job market with as much knowledge as possible. Easy-to-follow videos like this make that goal so much easier. Thank you!

Enthralling video on this topic. I cannot thank you more for the lucid explanation on this interacted topic.

Sublime. This topic just came up in a data analytics course I'm taking (it wasn't a central theme of the lesson, but I hate not knowing the details sometimes) and this programme is a perfect complement to that. Like others have said, your style is intuitive but not over simplified. In general, I feel like you're striking a great balance between ease of understanding and mathematical rigour.

Love the content. Easy to follow and understand. You are one of the best teachers in the data science field!

Amazing video, so clear and concise. I learn better with visual and conceptual ideas first before diving into the maths. Thank you!

best explanation on KDE I have seen

Amazing video! Clearly explained with an easy to understand example. Thank you

Quality content here. Also examples are nice and clear!

Really nice explanation. Thanks a lot.

Love it, amazing work in this video, congratS!

the only video i undestood without mathematical jargon.

Very good and intuitive explanation

Great explanation! Gaussian KDE is great for bimodal and skewed distributions. One downside with gaussian KDE is difficulty accurately modeling distributions with high excess kurtosis.

Amazing video thanks!!!!

Very clear flow of explanation, thank you. I'm thinking that it would be useful to design a hypothesis test for the chosen setup to back up the idea of the final density and so to get an extra information along with the vertical position of the chosen point as of how much proof we have for the final result that is allowed by the number and positions of the known fixed points. More research would be nice.

thank you

Clear explanation and easy to follow, thank you! Silly observation: "Integrate over all possible weights of fish. All the way from negative infinity to positive infinity": I'm no ichthyologist or fisherman but I feel negative weight fish ain't an option.

Thank you, Great Video:)

Great video

loved the way teach

Thanks for the good explanation about KDE method. could you please make a video about prediction intervals PI that sometimes uses the KDE method? thanks!

So helpful, better than my professor lol

very very good one pound fish

great video but i am confused about why we didn't use just 1/n*(sigma(...)) for MISE formula but integral and expected value.

Wow you are great can you make full Videos about ml using book An Introduction to Statistical Learning - with Applications in R?

Question, when conducting MC and sampling, can you use a KDE as a valid PDF as opposed to assuming a distribution (e.g normal, log normal, etc.)? Also, could this be considered kind of like a 1-d k means?