Building Practical Skills in NLP and Generative AI

Welcome to the 3-day intensive course on Building Practical Skills in NLP and Generative AI! This course is designed to equip you with a deep understanding and practical skills in the latest developments in Natural Language Processing (NLP) and Generative AI technologies.

By exploring foundational principles, advanced techniques, and real-world applications, this course will enable you to navigate and harness the capabilities of state-of-the-art AI models.

Building Practical Skills in NLP and Generative AI Benefits

• This course will address the following pain points:

  • Foundation Building: Gain a solid understanding of the core concepts behind Generative AI and NLP.
  • Advanced Techniques: Learn about the latest advancements in AI technologies including Transformers, GPT, and BERT architectures.
  • Hands-On Application: Participate in hands-on labs to apply concepts in real-world scenarios.
  • Industry Insight: Understand the applications of these technologies across various industries.

• Training Prerequisites

  • Basic knowledge of Python programming is required as labs and examples use Python.
  • Familiarity with general machine learning concepts is recommended but not essential.
  • No advanced mathematical or deep learning knowledge is required upfront.

Skills in NLP and Generative AI Training Outline

Day 1: Foundations of Generative AI and NLP Basics

Module 1: Introduction to Generative AI

• Overview of Generative AI and its evolution.
• Introduction to Large Language Models (LLMs).

Module 2: Core Concepts of NLP

• Understanding Tokens, Embeddings, and Transformers.
• Architectural insights into NLP systems.

Module 3: Practical Applications

• Exploration of real-world applications of LLMs in various sectors.
• Future visions in AI technologies.

Lab 1: Hands-On with LangChain and VectorDB

• Using LangChain tools and VectorDB for enhanced NLP workflows.

Day 2: Deep Dive into Prompt Engineering and Advanced NLP

Module 4: Prompt Engineering Essentials

• Fundamentals of crafting effective prompts for AI.
• Techniques for refining AI outputs and iterative prompt engineering.

Module 5: Advanced NLP Techniques

• In-depth exploration of Bag-of-Words, TF-IDF, and modern word embeddings.
• Utilizing Python for complex NLP tasks.

Lab 2: Building Advanced NLP Models

• Implementing practical NLP solutions using advanced techniques.

Module 6: Introduction to Sequential Models

• Deep dive into RNNs, LSTMs, and the use of attention mechanisms.

Lab 3: Implementing LSTM for Text Generation

• You'll get hands-on experience with LSTMs by using them to generate text.

Day 3: Exploring Advanced Architectures and Predictive Analytics

Module 7: Understanding Advanced Generative Models

• Overview of Seq2Seq, Autoencoders, and the innovation of attention in these models.

Lab 4: Implementing a Seq2Seq Model for Machine Translation

• In this lab, you will use a Seq2Seq model to build a simple machine translation system.

Module 8: Deep Learning Architectures

• Comparative analysis of GPT and BERT architectures.
• Understanding their applications and advancements.

Lab 5: Applying LLMs in Predictive Analytics

• Practical session on leveraging LLMs for data augmentation and analysis.

Module 9: Future of AI and Wrap-Up

• Discussions on the ethical implications and future trends in AI.
• Review of the course content and guidance for further learning.