Learn classical NLP, embeddings, transformers, and evaluation techniques beyond large language models
Sub Category
- Data Science
{inAds}
Objectives
- Design robust NLP pipelines from raw text to model input
- Apply text preprocessing, tokenization, parsing, and normalization correctly in production settings
- Build and evaluate classical NLP systems using Bag-of-Words, TF-IDF, and statistical features
- Understand and implement word embeddings, sentence embeddings, and document embeddings
- Use transformers for understanding tasks, not just text generation
- Choose the right encoder-only, sequence, or attention-based model for a given problem
- Evaluate embeddings using intrinsic and extrinsic metrics, while accounting for bias and representation risks
- Think like an AI Engineer, not just a model user
Pre Requisites
- Basic Python programming
- Fundamental understanding of machine learning concepts
- Curiosity to understand how AI systems actually work
- No prior NLP experience is required—everything is built step by step
FAQ
- Q. How long do I have access to the course materials?
- A. You can view and review the lecture materials indefinitely, like an on-demand channel.
- Q. Can I take my courses with me wherever I go?
- A. Definitely! If you have an internet connection, courses on Udemy are available on any device at any time. If you don't have an internet connection, some instructors also let their students download course lectures. That's up to the instructor though, so make sure you get on their good side!
{inAds}
Coupon Code(s)