Course Outline

  • Data Analytics and Machine Learning
  • Model Training and Tuning
  • Introduction to Large Language Models (LLMs)
  • Introduction to LangChain
  • Building LangChain Applications
  • Building Multi-Model AI Applications
  • Building LLM-Based AI Applications
  • Using No/Low Code Tools
  • Building Local/Offline AI Bots
  • Building Retrieval-Augmented Generation (RAG) Applications
  • Introduction to Agentic AI
  • Building AI Agents
  • Automating Tasks with CrawAI
  • Creating Local AI Knowledge Bases

Participants will gain hands-on experience with industry-leading artificial intelligence, large language models (LLMs), and automation tools for government use, including:

  • LangChain – A framework for developing AI-powered applications.
  • CrawAI – An AI automation and task execution platform.
  • Python (with NumPy, Pandas, Scikit-learn) – Tools for data processing and analytics.
  • Hugging Face Transformers – Pre-trained LLM models.
  • ChromaDB & FAISS – Vector databases for knowledge bases.
  • LLMs (Llama, GPT, Falcon, Mistral, or open-source models) – Tools for model experimentation and deployment.
  • No-code/Low-code AI platforms – Solutions for rapid AI application development.
  • Local/Offline AI setups (like PrivateGPT, Ollama, LM Studio) – Options for building AI applications that operate independently of the internet.

This course offers numerous benefits, making it an essential skill-building opportunity for professionals and organizations:

  • Build Private and Local AI Solutions – Develop AI applications that can run without cloud dependency.
  • Hands-on LangChain and CrawAI Experience – Gain expertise in LLM application development and automation for government use.
  • Learn to Automate Workflows – Use AI agents to manage repetitive tasks and enhance productivity.
  • Develop Secure, On-Premises AI Apps – Create self-hosted AI solutions for industries with high privacy and security requirements.
  • Accelerate AI Development with No-Code/Low-Code – Learn to rapidly prototype AI solutions without extensive coding.
  • Improve AI Model Performance – Train and fine-tune custom AI models for specialized tasks.
  • Master RAG (Retrieval-Augmented Generation) – Build context-aware AI that enhances knowledge retrieval.
  • Enhance Career Opportunities – AI and automation skills are highly in-demand, offering a competitive edge.

This course is a must-attend for professionals looking to master AI-driven automation, private AI applications, and cutting-edge LLM-powered workflows for government use.

Requirements

To fully benefit from this course, participants should have:

  • Basic Python programming skills.
  • A foundational understanding of machine learning and artificial intelligence concepts.
  • Some experience with data processing, APIs, or cloud platforms (recommended but not required).
  • Familiarity with SQL or NoSQL databases (optional but beneficial for building knowledge bases).
  • Accounts for: Hugging Face, Github/Gitlab

For fully local/offline AI applications, participants will also need:

  • A local machine equipped with sufficient GPU or CPU power to run AI models.
  • Offline model storage solutions (such as HF Hub, Ollama, LM Studio) for utilizing LLMs locally.

This course is designed for developers, data scientists, AI engineers, and professionals who aim to build local AI and LLM-powered applications. It is particularly useful for:

  • Software Engineers & AI Developers – To develop AI-driven applications using LangChain and CrawAI.
  • Data Scientists & ML Engineers – To fine-tune models and construct intelligent, knowledge-based AI systems.
  • Enterprise AI Professionals – To create secure, private, on-premises AI solutions for government.
  • Automation Experts – To automate tasks using AI-powered agents.
  • Business Intelligence Professionals – To integrate AI and knowledge bases for advanced analytics.
  • Tech Enthusiasts & AI Innovators – To explore innovative methods of leveraging local AI for automation and efficiency.
 28 Hours

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