Introduction to AI, LLMs & LangChain
Beginner-friendly guide to AI, Large Language Models, and LangChain.
About This Course
Program Overview
Why Choose This Course?
No Math Required
Focus on concepts and application, not complex math.
Chat Models
Deep dive into ChatGPT and similar models.
Frameworks
Hands-on experience with LangChain.
What You'll Learn
Course Curriculum
- What is AI?
- Types of AI
- Narrow AI vs General AI
- AI vs Machine Learning vs Deep Learning
- Real-world applications
- Basic AI workflow
- What is Machine Learning?
- Types of ML
- Features, Labels, Training data
- Example use cases
- What is Deep Learning?
- Neural Networks overview
- Input layer, Hidden layer, Output layer
- Introduction to NLP (Natural Language Processing)
- What are LLMs?
- How LLMs work (high-level)
- Popular LLM: GPTs models & Open-source models
- Limitations of LLMs: Hallucination, Bias & Context limits
- API concept
- Setting up API keys
- Making first LLM request
- Parameters: Temperature & Max Tokens
- Handling responses
- What is prompt engineering?
- Types of prompts
- Writing effective prompts
- Common mistakes
- What is LangChain?
- Why use LangChain?
- LangChain architecture overview
- Installation & setup (Python / Node)
- LLMs in LangChain
- Prompt Templates
- Chains
- Basic workflow example
- Why memory matters
- Conversation memory types
- Building a chatbot with memory
- Document loaders
- Text splitters
- Chunking strategy
- What are embeddings?
- Vector similarity search
- What is RAG?
- Why RAG is important
- Building document Q&A system
- Practical implementation
- What are agents?
- Chains vs Agents
- Using tools (search, calculator, APIs)
- Building smart assistants
Frequently Asked Questions
₹3,500
₹12,000 71% OFFThis course includes:
- No Math Required
- Chat Models
- Frameworks
Upcoming Batches
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