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Building Practical Skills in NLP and Generative AI

Course Outline

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.
Course Dates - North America
Course Dates - Europe
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