Building Local AI/LLM using Langchain and CrawAI

To fully benefit from this course, participants should have:

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

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

• A local machine with sufficient GPU or CPU power for running AI models.
• Offline model storage (like HF Hub, Ollama, LM Studio) for using LLMs locally.

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

• Software Engineers & AI Developers – To build AI-powered applications with LangChain and CrawAI.
• Data Scientists & ML Engineers – To fine-tune models and build intelligent knowledge-based AI.
• Enterprise AI Professionals – To develop secure, private, on-premises AI solutions.
• Automation Experts – To automate tasks using AI-powered agents.
• Business Intelligence Professionals – To integrate AI and knowledge bases for analytics.
• Tech Enthusiasts & AI Innovators – To explore new ways of leveraging local AI for automation and efficiency.

This course is designed to provide a practical, hands-on approach to building local AI and LLM-powered applications using LangChain and CrawAI. Participants will explore data analytics, machine learning, and model training techniques before diving into LLM-based application development.

The course covers essential topics such as multi-model AI applications, no/low-code AI development, Retrieval-Augmented Generation (RAG), agentic AI, and AI automation with CrawAI. Participants will also learn how to build AI chatbots, AI-powered knowledge bases, and offline/local AI models to ensure privacy and efficiency.

By the end of the course, participants will be able to:

• Understand the fundamentals of data analytics, machine learning, and AI models.
• Train, fine-tune, and optimize local AI and LLM models.
• Develop LangChain applications for AI-driven automation.
• Build multi-model AI and LLM-powered applications.
• Utilize no-code/low-code solutions to accelerate AI development.
• Create local/offline AI chatbots and intelligent knowledge bases.
• Implement AI agents and automate workflows using CrawAI.
• Develop Retrieval-Augmented Generation (RAG) applications to enhance AI responses.

This course is ideal for professionals looking to develop private, offline, or on-premises AI solutions, automate workflows, and build advanced AI-driven applications using LangChain and CrawAI

• Data Analytics and Machine Learning
• Model Training and Tuning
• Introduction to LLM
• Introduction to LangChain
• Building LangChain Apps
• Building Multi-Model AI Apps
• Building AI, LLM Based Apps
• Using No/Low Code
• Building Local/Offline AI bots
• Building RAG Apps
• Introduction to Agentic AI
• Building AI Agents
• Automating Tasks using CrawAI
• Building AI local Knowledge Base

Participants will gain hands-on experience with industry-leading AI, LLM, and automation tools, including:

• LangChain – Framework for AI-powered applications.
• CrawAI – AI automation and task execution platform.
• Python (with NumPy, Pandas, Scikit-learn) – 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) – Model experimentation and deployment.
• No-code/Low-code AI platforms – For rapid AI app development.
• Local/Offline AI setups (like PrivateGPT, Ollama, LM Studio) – For building AI applications that run without internet dependency.

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

• Build Private & Local AI Solutions – Develop AI applications that run without cloud dependency.
• Hands-on LangChain & CrawAI Experience – Gain expertise in LLM application development and automation.
• Learn to Automate Workflows – Use AI agents to handle repetitive tasks and enhance productivity.
• Develop Secure, On-Premises AI Apps – Create self-hosted AI solutions for privacy and security-sensitive industries.
• 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 improves 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.