This specialization was excellent when it launched and it remains genuinely useful, but reviewing it honestly in the current landscape means acknowledging that natural language processing has changed more in the last few years than almost any other corner of machine learning, and that shapes how I would recommend it. The four courses build carefully, starting with classical methods like word vectors and probabilistic models, moving through sequence models, and arriving at attention and transformers, and that arc is the real reason to take it, because you finish understanding the lineage of ideas that produced today's language models rather than treating them as magic. Having Lukasz Kaiser involved, someone who actually co-authored the transformer paper, gives the later material an authority that few courses can match. The sequencing is thoughtful, each course genuinely sets up the next, and the blend of classical and neural approaches is something most modern NLP teaching skips entirely in its rush to get to the exciting parts.
Where I have to be straight with you is on currency. The specialization largely predates the large language model era, so it takes you up to the transformer but stops short of the world of pretrained foundation models, fine-tuning, and prompting where most practical NLP work now actually happens. Some of the early classical material, while educational, covers techniques that the field has quietly moved past. And it teaches the deep learning portions in Trax, a framework that never gained much traction, so you will be translating concepts into PyTorch later anyway.
None of that makes it a bad course, it makes it a foundations course, and a good one. If you want to understand why modern language models work the way they do, this is a strong and rigorous path. If your goal is simply to build things with today's models as quickly as possible, a focused course on LLMs and prompting will serve you better, and you can always come back here when you want the deeper picture.