NLP Fundamentals Micro-Credential

NLP Fundamentals Micro-Credential

Duration: 3-4 Hours

Description

Every LLM, chatbot, and search feature ultimately rests on natural language processing — the discipline of turning text into something a computer can actually compute over. This session builds that foundation: vectorization (how words become numbers), sentiment analysis, and the more advanced techniques that power modern systems, including named entity recognition and the transformer architecture behind today’s language models. Participants build a working sentiment analyzer and apply real text-processing workflows, so the concepts connect to something concrete rather than staying theoretical.

Audience

This session is for developers new to NLP specifically — prior programming experience (Python is the most common fit) is required, but no prior NLP or machine learning background is assumed. It’s a strong fit for developers who use NLP-powered tools and APIs regularly and want to understand the mechanics underneath them.

Objectives

  • Define the core tasks of NLP and why it’s a challenging field.
  • Implement fundamental NLP techniques such as tokenization and stemming.
  • Understand the basics of text classification and sentiment analysis.
  • Use a popular NLP library to build a simple text processing application.
  • Begin to understand how more advanced models like Transformers work.

Prerequisites

Basic programming experience, preferably in Python, and a general understanding of data handling are recommended. No prior NLP or machine learning background is required, making this program accessible to developers, analysts, and data enthusiasts eager to work with text-based data.

Related AI Courses

See the full AI training roadmap and course directory for how this fits into a broader learning path.

For the transformer models that power modern NLP, see The Hugging Face Ecosystem. Curious about the fairness and bias considerations that apply to language models specifically? See AI Ethics and Bias Micro-Credential.

Course Outline

Module 1: The Building Blocks of Language

  • What is NLP?
  • The Challenges of Human Language
  • Processing Text
  • Lab – Process a Block of Text

Module 2: Extracting Meaning from Text

  • Vectorization
  • Text Classification
  • Sentiment Analysis
  • Lab – Build a Sentiment Analyzer

Module 3: Beyond the Basics

  • Named Entity Recognition (NER)
  • Word Embeddings
  • Language Models and Transformers
  • Advanced Topics