Update: I got an A with a 66.67%. The exams ate me alive, but if you put in the effort, you’ll be fine. Another thing I can say is that I won’t be taking more courses from Isbell. He broke me.
Taking this course now and almost done with Asg. 1. It's super tedious and mentally draining, but I knew about the tough grading before going in. This video reassures me that it will be ok. Thank you.
Hey, thanks for the review! It's been really helpful. I'm currently taking a graduate algorithm class and planning to take this one next. I'm doing okay in the algorithm class, but I'm not that great at coding. Do you think I need to take a Python coding course beforehand, or can I catch up on coding during the program? Thanks!
Hi Mohsen, great question. Yeah I would suggest doing a mini course in Python to get ready. It will just make your life easier once you actually start the class
Can you suggest a few public database that would be categorized as not so simple and not so complex for supervised learning assignments. I chose Breast Cancer Wisconsin (Diagnostic) Dataset for now@@coolstercodes
Nice Review! I'm planning on taking this class Spring 2024 (done ML4T, RAIT so far) and have experience using python, but not with frameworks/libraries such as Pytorch, or Abagail. I want to do a brief prep a month before the course. How skilled does one need to be in these tools to excel in the projects? And would watching the lectures beforehand be more valuable than learning them?
Thanks! Great questions I would say, that prior experience with Pytorch would be beneficial, just to get an idea of how it works high-level, before trying to implement something with it in the class (even just like YoutTube videos or tutorials of it). Abagail on the other hand isn't really necessary to have experience in before hand. Reason I say that is it's used in the last project (or I guess it's up to you to decide which projects you want to use it in, I used it in the last one) so if you learn it before the class, you may just end up re-learning it again at the end of the class But Pytorch would be good to get a basic understanding of, it will make things easier to debug while you're taking the class And watching the lecture videos, in my mind, would be most helpful during the projects, because you'll remember the details better while you're coding and debugging your projects Doesn't hurt though if you want a sneak peek Good luck and thank you for watching!
First of all, thank you for this post. Your previous post for CN was of great help as well! Question - Is this course manageable for someone who does not have any background on ML? Any tip on how to get upto speed? (I am picking this course in the fall)
Thanks! I think a good way to get up to speed would be to watch the lecture videos online (they are public on the GT website for the course) and start to just read on what all the terms mean (not even coding at first)
Hey Paul! Thank you very much for this review. I have a question about exams format: do we need to draw something or imitate the drawing textually? do we need to translate formulas to text? That's my biggest fear because in lectures professors are drawing everything by hand, and they hinted input will be only textual on the exam.
Thanks for posting this video. I have few questions though, 1. Is this degree treated the same as a regular degree? Can I use this degree for a PhD at another university? Can I migrate to countries like the UK and Australia, where they have points for Master's degrees? 2. Will this degree benefit my job hunting or help me shift my career from data engineering to data science?
These are really good questions, for #2 I will make a video dedicated to that so stay tuned For #1 yes this degree is treated as a regular degree, for all I know yes you can and the international question is new to me but I bet you could since this degree is completely accredited by the Southern Association of Colleges and Schools which is recognized by the United States Department of Education (about as accredited as you can get haha)
Nice video. How would you advice we prep for the finals? The projects have not given me much room to study the material. How best can we 'speed though' the materials in 2 weeks to prep for the final exam?
I was thinking to get this course in my first semester as my first course. Is it gonna be too tough? (I have a bs in software engineering, I am not that familiar with python. Fluent in javascript and c++.)
It honestly doesn’t use a lot of math, the most it ever got to was decreasing the probability of an event from like 0.9 to 0.1 over time, I.e a for loop decreasing the number little by little
