3.9 Natural Language Processing

Dependencies

Refer to the following markdown file for the respective sections of the class:

• Self Studies
• Lesson
• Assignment
• Quiz
• Reference

Lesson Objectives

Learners will understand:

• Definition, Historical Context, Challenges and Applications of Natural Language Processing (NLP)
• Text Preprocessing
• Text Normalization
• Language Modeling
• Vector Space Model and Word Embeddings
• Text Classification

Learners will be able to:

• Preprocess text data
• Normalize text via stemming and lemmatization
• Build a language model
• Measure similarity between documents via vector space model
• Find similar words via word embeddings
• Classify text via Naive Bayes

Lesson Plan

Duration	What	How or Why
- 5mins	Start zoom session	So that learners can join early and start class on time.
20 mins	Activity	Recap on self-study and prework materials.
40 mins	Code-along	Part 1: Introduction to NLP and Text Preprocessing.
	1 HR MARK
30 mins	Code-along	Part 2: Text Normalization.
10 mins	Break
20 mins	Code-along	Part 3: Language Modeling and Vector Space Models.
	2 HR MARK
50 mins	Code-along	Part 4: Word Embeddings and Text Classification.
10 mins	Briefing / Q&A	Brief on references, assignment, quiz and Q&A.
	END CLASS 3 HR MARK