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Comprehensive Generative AI for Developers

This Comprehensive Generative AI training course explores the foundations and applications of generative AI (GenAI), starting with core AI/ML concepts and progressing through large language models...

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Course Code WA3590
Duration 5 days
Available Formats Classroom, Virtual

This Comprehensive Generative AI training course explores the foundations and applications of generative AI (GenAI), starting with core AI/ML concepts and progressing through large language models (LLMs) and prompt engineering. Participants learn how to use natural language processing (NLP) techniques, build applications using LangChain, and implement retrieval augmented generation (RAG). The course also covers GenAI security concerns, LLMs via APIs, and advanced methods for diverse AI tasks

Skills Gained

  • Define core concepts of artificial intelligence and machine learning
  • Explain the mechanisms and types of large language models
  • Develop effective prompt engineering strategies for various NLP tasks
  • Implement retrieval augmented generation techniques for knowledge-based applications
  • Build and scale LangChain applications for complex problem-solving
  • Analyze security risks and defensive strategies for generative AI
  • Integrate LLMs through APIs and function calling for diverse applications

Prerequisites

  • Practical experience in Python (at least 6 months): Data Structures, Functions, Control Structures, Exception Handling, File I/O, async, concurrency (recommended)
  • Practical experience with these Python libraries: Pandas, NumPy, and scikit-learn
  • Understanding of Machine Learning concepts - regression, clustering, classification
  • Understanding of ML Algorithms: Gradient Descent, Linear Regression
  • Understanding of Loss Functions and evaluation metrics

Course Details

Introduction to Generative AI

  • What is Intelligence?
  • Mechanisms of Intelligence
  • What is Artificial Intelligence?
  • How does AI work?
  • Applications of AI
  • Machine Learning and its Types
  • Emergence of Generative AI

Understanding Large Language Models

  • What is a Language Model?
  • Natural Language Processing (NLP)
  • Tokenization: Approaches and Strategies
  • Word and Sentence Embeddings
  • Types and Training of Large Language Models
  • Applications and Value Proposition of LLMs

Accessing LLMs through API

  • Closed Source vs. Open Source LLMs
  • Accessing LLMs via API and Benefits
  • Prompt Templates and Structures
  • Function Calling and Integration with LangChain

Basics of Prompt Engineering

  • The Art and Science of Prompt Engineering
  • Communicating with LLMs
  • Principles of Effective Prompts
  • Common NLP Tasks and Strategies

Prompt Engineering for Engineers

  • Introduction to Code Generation
  • Effective Code Generation Techniques
  • Advanced Prompt Engineering Methods
  • Visualizing Chain of Thought

Introduction to Retrieval Augmented Generation

  • Understanding RAG and its Advantages
  • Vector Embeddings and Integrating with Knowledge Bases
  • Document Indexing and Retrieval Techniques
  • Managing Large Documents and Knowledge Bases

Advanced RAG Techniques

  • Enhancing Retrieval Accuracy and Performance
  • Handling Multilingual Retrieval and Error Management
  • Query Construction and Mitigating "Lost in the Middle" Effect

LangChain Agents

  • Introduction to LangChain Framework and Tools
  • Building and Scaling LangChain Applications
  • State Management and Multi-Tool Coordination

Generative AI Security and Privacy

  • Importance and Potential Risks of GenAI Security
  • Key Vulnerabilities and Attack Vectors
  • Principles and Defensive Strategies for Security

Conclusion

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