Thank you for creating this amazing resource!

Thank you for providing these materials for free!!

great effort .thanks

Amazing course. If possible, could you please make courses on applied DL and RL as well ?

Sir,Can you share some resources to learn the Applied ML like some standard books like you learned from because great explanation on Ml thank you sir

Could you post the homeworks/Assignments ? please🙏

thank dr. Kuleshov . will you share the code examples and slides to public as well and where

Hi just wondering do you have the slide/notebook shared as well? i think it will be great to have. Thanks!

It is good, there is such extensive course online and free. I will use the Pros/Cons system from this course for my review of this course: Pros: - it covers a lot of topics in ML - good to gain overall general knowledge about ML - I liked recap of previous lectures at the beginning of next lectures, it is good to know how lectures are connected - I liked summary for each algorithm at the end of lecture, what is it good for, how performant it is, how useful/popular it is, what are it's weaknesses - may be I would give it at the beginning so viewers will be curious about algorithm for the whole video and not after video ended - I liked how different approaches were applied to the same dataset (Iris flowers), so results could be compared - simple topics (eg. decision trees, testing methodology, ... ) were explained clearly and in understandable way Cons: - complex topics (eg. neural networks) were explained in too shallow and anectodical way, like if you speak to someone, who already knows, what is it about, so they are not possible to understand from this course - sometimes it is not going from simplest to more difficult, but opposite way - starting neural networks with artificial neuron and its relation to human neuron is just fail (this can be said, when neural networks are explained) So overall B-. A lot of topics covered, good knowledge shared, however some pedagogical mistakes makes this course occasionally not understandable.

Can you explain a little bit how this course is different from Machine Learning for Intelligent Systems CS4780/CS5780

Lecture 19. Part 2 is missing?

any link for notebooks ?

link for notebooks pls

Difference between machine learning and applied Machine learning?

I was moved with this intro...but please do i need any basic knowledge in programming?, or what are the perquisite to learn ML. Thanks for your quick response.

1.25x his energy increased

link for notebooks pls