Intermediate

NLP and Language Modeling

Dive into natural language processing, learning about embeddings, language models, and how to work with text data effectively. Prepare for understanding LLMs.

Estimated Time 28 hours

Introduction

Dive into natural language processing, learning about embeddings, language models, and how to work with text data effectively. Prepare for understanding LLMs.

4 Lessons
28h Est. Time
4 Objectives
1 Assessment

By completing this module you will be able to:

Master word embeddings and contextual representations
Understand language model training and next-token prediction
Implement sequence-to-sequence models
Work with tokenization and preprocessing for NLP

Lessons

Work through each lesson in order. Each one builds on the concepts from the previous lesson.

1

Text Processing and Tokenization

50 min

Start Lesson
2

Text Classification and Sentiment Analysis

50 min

Start Lesson
3

Named Entity Recognition and Information Extraction

50 min

Start Lesson
4

Text Generation and Summarization

50 min

Start Lesson

Recommended Reading

Supplement your learning with these selected chapters from the course library.

📖

Mastering NLP from Foundations to LLMs

Chapters 1-4

📖

Transformers for NLP and Computer Vision 3e

Chapters 7-10

Module Assessment

NLP and Language Modeling

Question 1 of 3

What is the main difference between word embeddings like Word2Vec and contextual embeddings like BERT?