Natural Language Processing Communication Revolution with AI #254041

Upon completion of this course, participants will be able to:

• Understand Core NLP Concepts: Define key NLP tasks such as sentiment analysis, named entity recognition (NER), text summarization, and machine translation.

• Grasp the Technology Behind the Scenes: Explain the fundamentals of how NLP models work, from traditional bag-of-words models to modern Transformers and Large Language Models (LLMs).

• Identify Business Applications: Evaluate and propose high-impact NLP use cases across various functions like marketing, customer service, operations, and R&D.

• Work with NLP Tools and APIs: Gain hands-on experience using pre-built cloud APIs (e.g., from Google Cloud NLP, AWS Comprehend, OpenAI) to perform common NLP tasks without deep coding knowledge.

• Manage an NLP Project Lifecycle: Understand the steps involved in developing an NLP solution, from data collection and model selection to deployment and ethics review.

• Navigate Ethical Considerations: Identify and address critical issues like bias in language models, data privacy, and the responsible deployment of NLP technology.

This course is designed for professionals who want to understand and apply the power of language-based AI:

• Product Managers & Product Developers

• Marketing Professionals & Content Strategists

• Customer Experience (CX) & Support Leaders

• Data Analysts & Business Intelligence Professionals

• Software Developers & Engineers (non-NLP specialists)

• Digital Transformation Consultants & Strategists

• Entrepreneurs looking to build AI-powered products

• Researchers & Academicians in social sciences and humanities

• Corporate Communicators & Public Relations Specialists

• Pre-assessment
• Live group instruction
• Use of real-world examples, case studies and exercises
• Interactive participation and discussion
• Power point presentation, LCD and flip chart
• Group activities and tests
• Each participant receives a binder containing a copy of the presentation
• slides and handouts
• Post-assessment

Day 1: Foundations of Language and AI

• AM: The NLP Revolution

  • Introduction: From Eliza to ChatGPT – a brief history of NLP.

  • Why NLP is hard: Ambiguity, context, and sarcasm in human language.

  • Core Concepts: Tokens, stems, lemmas, and stop words.

• PM: How Machines “Read” Text

  • Traditional Techniques: Bag-of-words, TF-IDF, and n-grams.

  • Introduction to Word Embeddings: How words are represented as vectors (Word2Vec, GloVe).

  • Hands-on Demo: Using a simple tool to visualize word embeddings and similarities.

Day 2: Core NLP Tasks and Techniques

• AM: Understanding Text and Intent

  • Sentiment Analysis: Determining positive, negative, or neutral opinion.

  • Named Entity Recognition (NER): Identifying people, places, organizations.

  • Workshop: Using a cloud API to perform sentiment and NER analysis on product reviews.

• PM: Structuring the Unstructured

  • Topic Modeling: Automatically discovering themes in a large collection of documents (e.g., LDA).

  • Text Classification: Categorizing text (e.g., spam detection, ticket routing).

  • Case Study: How news agencies use topic modeling to track story trends.

Day 3: The Deep Learning Leap: Transformers and LLMs

• AM: The Transformer Architecture

  • The breakthrough that changed everything: Attention is All You Need.

  • How Transformers understand context better than previous models.

  • Introduction to Hugging Face and the model hub.

• PM: The Era of Large Language Models (LLMs)

  • Understanding GPT, BERT, and other foundational models.

  • Prompt Engineering 101: The art of crafting instructions for LLMs.

  • Hands-on Lab: Basic prompt engineering with an LLM interface to perform tasks like summarization and question answering.

Day 4: Building Real-World NLP Applications

• AM: Conversational AI and Chatbots

  • Designing effective dialogue systems: intent recognition, entity extraction, and response generation.

  • The difference between rule-based chatbots and generative AI assistants.

• PM: Advanced Applications

  • Machine Translation: How tools like Google Translate work.

  • Text Summarization: Extractive vs. abstractive summarization.

  • Workshop: Building a simple text summarization pipeline using an API.

Day 5: Strategy, Implementation, and Ethics

• AM: The NLP Project Lifecycle

  • From idea to deployment: Scoping, data sourcing, tool selection, and integration.

  • Build vs. Buy: When to use APIs vs. when to train custom models.

• PM: Responsible NLP and Capstone

  • Ethics in NLP: Tackling bias in training data and model outputs, ensuring fairness, and protecting user privacy.

  • Capstone Project: In teams, design an NLP solution for a business problem (e.g., an automated insight generator for customer feedback). Present the concept, intended NLP techniques, and an ethics review.

  • Course Wrap-Up: The future of NLP and creating a personal action plan.