LangChain Fundamentals

  • IntermediateLevel

  • 1079+Students Enrolled

  • 2 Hrs 30 MinsDuration

  • 4.5Average Rating

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About this Course

  • Master the core LangChain fundamentals and understand how to build intelligent applications powered by LLMs, through guided hands-on lessons.
  • This LangChain certification course helps you gain the technical foundation to design implement, and scale LLM-based workflows that automate tasks, reasoning, and decision-making .
  • Explore real-world use cases, from conversational AI to data-aware agents, learning to integrate APIs, vector databases, and prompt templates effectively.
  • Perfect for developers and AI enthusiasts, this LangChain course for LLM app development equips you with the tools, frameworks, and confidence to build production-ready gen AI app.

Learning Outcomes

LangChain Core Concepts

Understand LLMs, chains, agents and memory in LangChain workflows.

LCEL & LangGraph Flow

Use LCEL and LangGraph to build LLM application flows.

RAG & Vector Integration

Implement document ingestion, vector stores and RAG pipelines.

Who Should Enroll

  • Beginners curious about LLMs and looking to explore how tools like LangChain simplify AI app development.
  • Developers and data enthusiasts wanting a hands-on intro to LangChain, LangGraph, and LCEL without deep ML expertise.
  • Students and learners aiming to build simple, real-world GenAI apps using modern AI workflows.

Course Curriculum

Explore a comprehensive curriculum covering Langchain fundamentals, machine learning models, deep learning techniques, and AI applications.

tools

  1. 1. Introduction to the Course

  2. 2. Introduction to the Langchain

  3. 3. Introduction to LangGraph

  4. 4. Introduction To LangSmith

  5. 5. Introduction To LangServe

  6. 6. Setting up LangChain for LLM App Development

  1. 1. LangChain Legacy Syntax

  2. 2. LangChain Expression Language (LCEL)

  3. 3. Hands On - LangChain Expression Language (LCEL)

  4. 4. Let's Compare LangChain Then Vs LangChain Now

  1. 1. LLMs and Chat Models

  2. 2. Prompting with Prompt Templates

  3. 3. Format LLM Response with Output Parsers

  4. 4. LLM Advanced Operations

  1. 1. Introduction to Retrieval

  2. 2. Document Loaders

  3. 3. Document Splitters and Chunkers

  4. 4. Embedding Models

  5. 5. Vector Databases

  6. 6. Retrievers

Meet the instructor

Our instructor and mentors carry years of experience in data industry

company logo
Dipanjan Sarkar

Principal AI Scientist

Dipanjan Sarkar, Head of AI, Author & Consultant, has 12+ years of expertise in ML, DL, GenAI, CV & NLP. He has led AI initiatives across Fortune 100 firms & startups, building data products & upskilling professionals at all levels.

Get this Course Now

With this course you’ll get

  • 2 Hours 30 Mins

    Duration

  • Dipanjan Sarkar

    Instructor

  • Intermediate

    Level

Certificate of completion

Earn a professional certificate upon course completion

  • Industry-Recognized Credential
  • Career Advancement Credential
  • Shareable Achievement
certificate

Frequently Asked Questions

Looking for answers to other questions?

In the LangChain Fundamentals course, you’ll learn the core concepts of building LLM-powered applications, from prompt engineering to chaining logic, API integration, memory handling, and deploying real-world generative AI systems efficiently.

Basic Python knowledge helps.Though, step-by-step guidance ensures you can understand and implement LLM app concepts, even if you’re new to AI development.

For the LangChain fundamentals course, you’ll need Python, Jupyter Notebook, and access to OpenAI or other LLM APIs. All required installations and setups are explained step by step during the course.

The LangChain fundamentals course covers essential components like chains, memory, agents, vector stores, and prompt templates — helping you understand how to connect LLMs with APIs, databases, and external tools effectively.

Yes! The LangChain certification course emphasizes project-based learning. You’ll build mini AI agents, automate workflows, and develop data-aware apps to strengthen your practical understanding of LangChain’s framework.

The LangChain introduces vector databases like FAISS and Pinecone, teaching how they store embeddings for semantic search, allowing LLMs to recall relevant information quickly from large datasets.

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