Thank you @balaji.ambresh for adding me, actually I did come across this post, but didn’t read it through as I thought he was providing his experience to be an NLP expert.
Although I agree with @MarcoATL, people wanting to become NLP expert come with different level of experience, I would make prototype which mentions
a flow-chart, like if knows python programming, pytorch, basic tensorflow tf1 or general tf and keras, then probably @MarcoATL suggestions might fit in somehow.
But we actually never know how one makes a decision, so always provide the basic path to be an NLP expert mentioning significance of each course on how it might help in understanding NLP concept.
I have seen many experts here suggesting first MLS(Machine learning course) and then DLS course, but I kind of disagree on this because Prof.Ng has covered like every nuke and corner on how to handle data, first in DLS, so when I did MLS later, for me it was practice specialisation of what I learnt and understood in DLS.
For me TF1 would be necessary pre-requisite no matter you know python or keras or tf in general or not, to be an NLP expert. Here is my reasoning. TF1 was one of the best explained, very clear, easiest course which help me understand the concept i learnt about cnn, kernel, filter, data conversion, model training using tensor and keras from DLS which eventually will help in course 3 and course 4 of NLP specialisation to do more smoothly and understand on basic concepts as they updated with tensorflow version of assignment. NLP also convered sequence modelling which was easy go as it was thoroughly taught in DLS yet understanding of tensorflow helped me understand some of tensor data conversion flow and how inputs data is converted to tf type.
Now come to Computer vision which is part tf3 specialisation, this you could add as an optional course as I understand they aren’t related too literally but I personally love this course. it was the toughest course when I was naive in ML and AI, but practicing and teaching this, still makes the curious kid to explore more. My personal goal is healthcare, so for me computer vision was must as it cover Bounding box, transfer learning, object detection, auto-encoder visualisation which are must for any creator.
MLOps is again an advanced course which could be optional, to understand NLP.
Another thing @MarcoATL probably missed is adding the numerous selective short courses related NLP, as text, image and videos are converted into magical numerical for them to dance together, doing short course regarding LLMs is like I saw that concept in NLP, aa ha so temperature holds importance for the chatgpt to give me output relative to the deterministic or semantic, covering cosine similarity concept explained in NLP.
I have seen learners doing NLP even when they don’t know much about python, that’s a big no as they might end up upsetting themselves.
Learning becomes fun, exciting and growing when it is simple yet challenging, complex yet correlative for learner to understand and growing so they can create their idea from concepts of different specialisation they have learnt.
Namaste
DP