This is terrific. Any plans to do an update, as the Canvas has changed a bit in a couple of years.

Thank you so much. Excellent explanation!

Wow.. It looks realistic ❤

Incredible

Great tutorial, very well explained. Keep up the good work. Thank you.

I appreciate your explanation and teachings. Heard my colleagues talk about this often and I was curious to find more information which is really simplified for a none technical person like me. I am happy I watched this. Thank you

Clear explainantion of the Ridge() model. Very intuitive. SUBSCRIBED.

I recently completed the "Python, Data Science, & AI - Level 2" course on the CFA Institute website. I really enjoyed the course and learned a lot. I'm interested in continuing my education in this area. Do you have any suggestions for what I should study next?

Hello professor, the lecture is very good, can I have the slides please, thank you

How to use map instead of slicer for filtering ????

2 mins deep and I have the concept already... Great Job Professor.

Well explained. Can you donan example with balanced dataset and explain about f1 score in imbalanced dataset also ?

Amazing well explained

👍👍

Are you planning any update to this course?

Really Helpful video to get a glance on what to use when

Thank you so much for this. For a non-IT person trying to learn Python, I understood this finally!

Can you post the arcile link?

wow, great video

Aoa The provided link for material is not working please post a working link

RDS and Aurora are 2 different things

Mn

I think distance based algorithms required scaling. Please double check

جزاك الله خير بروف ريان, شرحك جدًا ممتع وواضح وطريقة تفسيرك للخطوات شيء هائل. شكرًا لك

Harris Carol Harris Edward Jackson Jason

Demonastery

the best explanation! finally understood, thanks!!

wow it wonderfull video my brother thanks to you describe prons and cons

finally understand the regularization after wasting my all day. this is by far the best video on topic. thanks sir

how can i download the csv file of the data u have used

Hi Prof Ryan, Please I want to know how could I reach you? Thanks in advance...

Amazing...

awesome..you are a gem sir

Thank you, Prof., for very well-articulated AWS Sage Maker.

Thx and god bless 😊

I love your teaching, straight forward and clear. Also, the visual presentation is very easy to understand.

hi sir! i like your videos so much, but you're not uploading anymore? how's it going?

You're the best prof!

Something is not right in this lecture. If each subsequent tree is _the_same_, as shown here, then after 10 steps the 0.1 learning rate will be nullified, e.g. equivalent to the scaling = 1.0! In other words, no regularization. Hence, trees must be different, right?

3:23

Beautifully Explained :)

It's was really helpful ❤

I really liked that you provided few examples for those more difficult distributions. What I would love to see is an explanation of where do the probability formulas in Poisson and Binomial distributions come from. I see that they are combinatorics formulas, but I'll have to check euler^(- miu) in Poisson.

standardization has nothing to do with normal distribution

Thank you

Very well explained, thank you

So say you pass two trainign examples, the gradients get calculated for example 1 and the weights are adjusted. Then the same happens for example 2, but what if the net gradient adjustments are 0, this would be a computational redundancy. Is there any area of research or techniques where you can prevent unnecessary gradient calculations?

a year later and chatgpt is still generating great code! It can write quite complex code - the context window is gradually increasing

and I thought my content is too colorful :)

You've just nailed it PROF!