NLP and Large Language Models
Gain the Technical Depth and Strategic Vision to Lead with LLM and Generative AI. This is a comprehensive course in AI specifically for Professionals and Technical Leaders focused on Natural Language Processing (NLP) and Large Language Models (LLMs). It is designed to provide you with a technical foundation in modern text analysis techniques (TF-IDF to Transformers) and the strategic expertise to evaluate build-versus-buy options in the LLM market. The course empowers you to develop, prototype, and deploy advanced NLP solutions through hands-on activities and a final product design capstone.
Who is this for?
Data Scientists, AI Engineers, Technical Product Managers, and Solution Architects who need a deep, practical, and strategic understanding of Natural Language Processing (NLP), Large Language Models (LLMs), and the surrounding ecosystem to design, build, and integrate these technologies into business products.
Prerequisites:
No formal advanced AI engineering prerequisites are listed, but basic familiarity with coding concepts is recommended due to the technical depth of topics like Neural Networks, LSTMs, and the BERT activity.
What You Will Achieve
Master the NLP pipeline from traditional techniques (TF-IDF, Bag of Words) to modern approaches (Word2Vec, Transformers).
Gain practical experience in building models using foundational Deep Learning architectures like Recurrent Neural Networks (RNNs) and LSTMs.
Confidently apply and fine-tune state-of-the-art LLMs using the HuggingFace library and models like BERT.
Develop strategic insight into Generative AI deployment, including the use of the ChatGPT API, Prompt Engineering, and mitigating business drawbacks.
Understand the LLM deployment ecosystem, including LangChain and Vector Databases, and use this knowledge to assess build vs. buy options.
Apply all concepts in a capstone project to design a new AI-powered product and present the solution.
Key Topics Covered
This 14-session, 1-hour-per-session curriculum (14 Total Hours) is structured to build knowledge from foundational AI to advanced LLM deployment:
Foundational AI & Data Science: Introduction to AI hierarchy, Supervised Learning/Classification (with sentiment detection activity), and the Data Science Pipeline within a business context, covering data best practices and tool landscapes.
Traditional NLP & Neural Networks: Introduction to TF-IDF, Bag of Words, and Text Pre-processing. Core concepts of Neural Networks (layers, architecture) and Word2Vec for pre-processing and transfer learning.
Deep Learning for Text: Introduction to Recurrent Neural Networks (RNNs) and LSTMs, exploring their application in NLP and their usage in business models.
Transformers and how they revolutionized NLP.
Overview of the HuggingFace library, BERT architecture, and pre-trained NLP models.
Activity: Train your own BERT using transfer learning.
Generative AI & Deployment: Introduction to ChatGPT operation and Prompt Engineering (with hands-on activity), the use of the ChatGPT API, and managing usage patterns.
Strategic Mitigation & Ecosystem: Discussion of ChatGPT drawbacks and mitigation strategies, overcoming automation issues, and introduction to LangChain and Vector Databases.
Build vs. Buy & Emerging Trends: Analysis of Build vs. Buy options for LLMs, an overview of a simple LangChain prototype, and a look at emerging trends like Explainable AI (XAI), Edge, and Quantum Computing.
Capstone Application: Apply the concepts to a real-world problem, design a product or service that incorporates the solution, and present the capstone for discussion.
Assessment & Certification
Assessment is based on hands-on activities (e.g., building a sentiment model, training BERT, and prototyping a ChatGPT/LangChain solution) and a final Capstone Project. You will design an AI product/service and present the solution to the class, emphasizing the practical application and strategic design aspects of LLM technologies.