Natural Language Processing

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.

Indicative Duration: 16 training hours
*Duration is adjusted based on the final scope and the target audience.

Scope

1. NLP intro and Vector Space Models
  • NLP basics: how to represent words with vectors. Bag-of-words & TF-IDF models
  • Hands-on document classification with TF-IDF
2. Trainable VSM
  • word2vec model: motivation, how it works, technical details
3. Sequential models
  • Machine translation case study, seq2seq models, attention mechanism
4. Attention models (intro)
  • High-level description of state-of-the-art attention based models: Transformers, Bert, etc.
5. Case study
  • Hands-on NLP example. Tips and tricks

Learning Objectives

Upon completion of the course participants will be able to:

  1. Understand the fundamentals of NLP and represent text using vector space models like Bag-of-Words and TF-IDF.
  2. Apply document classification techniques using TF-IDF in hands-on exercises.
  3. Explain and implement trainable vector space models such as word2vec.
  4. Explore sequential models including seq2seq and attention mechanisms through a machine translation case study.
  5. Gain a high-level understanding of attention-based models like Transformers and BERT.
  6. 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

Delivery Method

Sessions can be delivered via the following formats:

  • Live Online – Interactive virtual sessions via video conferencing
  • On-Site – At your organization’s premises
  • In-Person – At Code.Hub’s training center
  • Hybrid – A combination of online and in-person sessions

 

The training methodology combines presentations, live demonstrations, hands-on exercises and interactive discussions to ensure participants actively practice AI in realistic work scenarios.

Tags:

Date

On Demand

Organizer

Code.Hub
Email
[email protected]