Code-first, practical tutorials taking you from data visualization fundamentals to multi-agent architectures with LangGraph, local LLMs, and RAG systems.
Follow sequential roadmap models carefully designed to build core production skills.
A structured, progressive roadmap for developers seeking to master AI agents, covering Python foundations, LangChain, LangGraph, and private multi-agent RAG.
A comprehensive curriculum taking you from zero programming knowledge to professional data manipulation, mathematical visualization, and core ML model building.
Dive deep into specialized branches of Artificial Intelligence and Software Engineering.
LLMs, RAG, Agents, LangGraph & custom AI applications.
Data analysis, EDA, feature engineering & classic ML models.
PyTorch, neural networks, CV & advanced architectures.
Transformers, BERT, text analysis & sequence learning.
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Convert a FAISS vector store into an agent-accessible tool and build an Agentic RAG that autonomously retrieves document context only when relevant, with an interactive streaming chat loop.
Build autonomous LangChain v1 agents using create_agent — wire web search tools, configure model parameters, implement dynamic model switching, and stream agent responses.
Master LangChain Expression Language (LCEL) — build sequential, parallel, router, and custom chains using the pipe operator, RunnableParallel, RunnableLambda, and the @chain decorator.
Add persistent chat memory to any LangChain chain — store and replay multi-turn conversation history using RunnableWithMessageHistory and SQLChatMessageHistory.
Build a streaming, multi-session chatbot web app with LangChain, Ollama, and Streamlit — using persistent SQL memory and token-by-token streaming output.
Wrap the two-stage LLM resume parsing pipeline into an interactive web application using Streamlit, enabling users to upload PDFs and view extracted JSON data in real time.
Enroll in complete structured packages with production codebases, private GitHub repositories, and verifiable certificates.
Build MCP servers & clients with Python, Streamlit, ChromaDB, LangChain, LangGraph agents, and Ollama integrations.
Step-by-Step Guide to RAG with LangChain, LangGraph, and Ollama | DeepSeek R1, QWEN, LLAMA, FAISS.
Master Langchain v1, Local LLM Projects, Ollama, DeepSeek, LLAMA 3.2, Complete Integration Guide.
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