@Satoru Gojo , thanks a million for this upload.
please how can i find it to downloadInstead of asking for alternate links, try to find ways to download them by yourself. It's not that hard or expensive.
Buy an external SSD(always useful), Expand Google drive if necessary for atleast 1 month, Copy to Google Drive and download, ExtractHow do you guys download such big files? i tried to extract the files using google collab and was thinking of accessing this course from the drive itself but failed to do so. Can someone help on how did you download these files from google drive and how do you extract such huge chunk of data?
U copy 15 gb then 15 gb and so on ...How do you guys download such big files? i tried to extract the files using google collab and was thinking of accessing this course from the drive itself but failed to do so. Can someone help on how did you download these files from google drive and how do you extract such huge chunk of data?
its 226GB after extraction.Can you tell me how big is it? I imagine based on their website must be close to 100GB or even more. CHecked a few videos on youtube, speaks OK, and there seem to be quite a few projects after all that basic python, oop, numpy, pandas crap that by now anyone interested in ds / ml / ai should know at least fundamentally.
Anyway -- can someone tell us if it worth to grab it instead of the zero to hero pytorch | tensorflow | machine learning / data science course, or even the 365 datascience classes that seems to have very similar content? What difference this one makes compared to those? Well, not comparing that this course is bragging anout 500+ hrs of content.... (I'd say that is a drawback not an advantage - speaking concisely is a virtue, while blabbling for hours on end is not)
I will not download this, just wondering if someone amongst the currently ~100 people who intended to download this have some opinion about this. Well, after the download completes, so maybe a week laterprobably.
The videos are 3-4 hours long therefore, the size. The course material is really good, it has live videos and the fast track courses inside. So if you want to speed up the process then just watch fast track course if you want the whole deal then go for live videos of 3-4hrs which I would recommend if you are a beginner. Explanations are sweet. And yeah you can finish it in 2-3 months Max.So almost like a quarter terabyte? WTF is it like 4k videos for handmade drawings and talking heads or what?
Or this must be watched at warp-speed so someone could finish it in their lifetime
@Lightning Storm - sorry will not download, this is just insanely too much data for such a course.
The only ones I find worth it is the DSAR section, where they discuss systems design and architecture of data science projects. The course indeed is a lot of value for a bootcamp, considering it's very cheap and covers a lot of ground, but unfortunately its live class-format gives it a lot of noise, 3 hours per video makes it hard to skim, find important content, and rewatch/review.You know what, I might take a look. though I prefer shorter more focused content, not 3-4 hrs long videos I tend to lose interest in half an hour or so. But I might take a look depending on how fast I can download it.
Yes, Alex has a new book on ML System Design. I havent checked it yet though, as I dont work in ML. But the design discussion at ineuron isnt really about interview style system design, but instead about architecture and about defining engineering and design requirements. Thus I find it more practical for work. Although, the duration makes it hard to watch, the whole DSAR section is about 30 hours, where he tends to go off-tangent a lot as well.Thanks for sharing your opinion. I thought about it similarly like you. For me the ideal content is the one like the guy at fireship.io does. No need to crank up the playback speed at allI type the code, stop the video and maybe rewind a bit time to time. Very practical, focused content.
And about system design, isn't the byte by byte guy (Alex Xu) one of the best in that regard? Or are there like tremendous differences with how an ML project is scaled built-up compared to anything else?