Natural Language Processing (NLP)
Description
This course introduces the core concepts and models in Natural Language Processing, from traditional vector space models to modern attention-based architectures. Participants will gain hands-on experience with tools like TF-IDF, word2vec, and seq2seq models, and explore real-world NLP applications.
Key Objectives
By the end of this module, participants will be able to:
- Understand the fundamentals of NLP and represent text using vector space models like Bag-of-Words and TF-IDF.
- Apply document classification techniques using TF-IDF in hands-on exercises.
- Explain and implement trainable vector space models such as word2vec.
- Explore sequential models including seq2seq and attention mechanisms through a machine translation case study.
- Gain a high-level understanding of attention-based models like Transformers and BERT.
- Apply NLP techniques in real-world scenarios using practical tips and hands-on case studies.
Target Audience
- Roles: Data Scientists, NLP Engineers, AI Researchers, Software Developers
- Seniority Levels: Intermediate to Advanced professionals with prior ML experience and interest in language technologies
Prerequisite Knowledge
- Solid Python programming skills
- Basic understanding of machine learning concepts
- Familiarity with linear algebra and probability
- Experience with libraries like scikit-learn or TensorFlow is a plus
Classroom
Sessions can be delivered:
- Live online via video conferencing platforms, with recording available for later review
- Interactive workshops with practical exercises, real-time demonstrations, and collaborative activities
- Hybrid approach combining live online delivery with on-site support if needed
The teaching methodology combines presentations, live demonstrations, hands-on exercises, and interactive discussions to ensure participants actively practice AI in realistic work scenarios.
