A progression from classical NLP workflow design to representations, classification, self-attention, and LLM context.
Build an NLP mental model in sequence: define the task, place preprocessing and representation in a pipeline, try a supervised text-classification framing, then connect self-attention to the historical development of language models. The selected CampusX lessons are instructional source material rather than a promise of a particular model outcome. Exercises focus on making preprocessing choices and evaluation assumptions visible.
Map an NLP task from raw text through preprocessing, representation, model, and evaluation. Explain the role and trade-offs of bag-of-words, n-grams, and TF-IDF. Frame text classification with a target, split, baseline, and error-review plan. Describe self-attention as a mechanism for relating tokens in context.
Basic Python and comfort with lists, strings, and simple classification terminology are recommended.
Beginners who want a clear, guided introduction without unnecessary jargon
People who learn best through examples, guided lessons, and hands-on practice
Professionals, creators, and independent builders looking for a repeatable workflow
Lesson 1 is free
7 lessons · Beginner · Full course.
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