FREE 11 Hour NLP Transformers Course (Next 3 Days Only)

Key Takeaways

i want to introduce you to this course i've been working on i've just released it and i wanted to give a lot of you guys who are subscribed and follow me on medium or twitter i wanted to give you guys a chance to get this course for free so for the next three days it is is completely free just use this code but i just want to talk very quickly about what it actually covers now obviously you can see from the title it's nlp and it's with transformers and python now if we scroll down a little bit we come to this course overview video and i'll just quickly you know go through this because it's quite long and i don't want to take too much time too much of your time and we cover a lot of things so first thing is nlp transformers where i give it a quick summary of nlp in general the history of end up with leading up to transformers then moving to a bit of pre-processing for nlp now this is just your basic stuff i think the most relevant one here for us and transformers is unicode normalization and tokenization special tokens then i move through a few lectures on attention how attention works and describing the logic behind it before moving into what i i always see this is like the hello world of nlp which is sentiment analysis i think it's a great introduction and we introduce transformers in this section here and it's worth pointing out as well that i use a lot of different frameworks throughout this course so flare is the very first one we also we use face transformers that's the obviously the primary one that we'll be using throughout the course tensorflow pi torch nltk spacey and and many others as well so there's a lot in there of course using a lot of bert then there's a few so there's two projects in the course as well the first of those sentiment analysis the second one is question answering both of them i think are great because they take you all the way through from the very start of your project so getting data all the way through to actually building your model and applying it to your data then we moved on to named entity recognition question answering how we measure the performance of our models which is of course very important a full question answering stack using some another library called haystack which i think this is one of the coolest things in the course in my opinion and it just in general in nlp in general this sort of stuff is incredibly cool then like i said there's that second project the the q a project before we move on to similarity now similarity is super important in nlp and i think probably one of the most promising areas in the future for further research and just impact that he could have on industry i think this is really a super cool place to be then finally we move on to fine tuning so that's the course in a nutshell all together there's 11 hours of content so it's i think comparatively long when you look at other nlp courses so you know we see this 11 10 10 3 and 6 and as far as i'm aware it's the first course that focuses on transformers on udemy so if you're into nlp obviously transformers are really the models that you want to be using you know check out the course the next few days it's completely free using this code so thank you for watching and i hope you enjoy the course