Skillget

AI-Season-Course-Material

mcp
Guvenlik Denetimi
Basarisiz
Health Uyari
  • No license — Repository has no license file
  • Description — Repository has a description
  • Active repo — Last push 0 days ago
  • Community trust — 11 GitHub stars
Code Basarisiz
  • exec() — Shell command execution in ARGPT/coding_llm/build_data2.py
  • eval() — Dynamic code execution via eval() in ARGPT/coding_llm/chat7.py
  • exec() — Shell command execution in ARGPT/coding_llm/evaluate8.py
  • eval() — Dynamic code execution via eval() in ARGPT/coding_llm/train6.py
Permissions Gecti
  • Permissions — No dangerous permissions requested

Bu listing icin henuz AI raporu yok.

SUMMARY

Runnable code from the AI Season AI Agents Bootcamp (Cohort 01): LLM APIs, RAG, agent harness, LangChain, multimodal agents, A2A, and an LLM from scratch. Taught in Urdu + English.

README.md

AI Season — AI Agents Bootcamp: Course Material

Runnable code from AI Season Cohort 01 (July–August 2026) — a live
online AI agents bootcamp taught in Urdu and English by
Abdul Rahman Azam from Karachi, Pakistan.

Every folder is a self-contained lesson: numbered scripts you run in order, a README that explains
what each file teaches, and a requirements.txt. The code is written for teaching — flat, readable
scripts rather than a framework you have to understand first.

Learn it live: Cohort 02 of the AI Season bootcamp is enrolling now — 6 weeks, 12 live
sessions, explained in Urdu with English code, open to students in Pakistan, India and worldwide.
Details at aiseason.tech.

What's inside

Module What you learn Key tools
Session 1 — LLM API basics First model call, streaming, chat history, parameters (Python + JavaScript) Model provider SDKs
session 3 — RAG over your documents File conversion, chunking, embeddings, vector store, retrieval methods, hybrid search, answer generation, failure experiments, a Streamlit chat app LangChain, Chroma, local embeddings, Groq
Session 4 — Build an agent harness The loop that turns an LLM into an agent: tools, memory, safety rules, a verifier and tests, built checkpoint by checkpoint Python, pytest
session 5 — Multimodal agents Vision-language models, audio, video, OCR pipelines, document understanding, multimodal RAG, tool calling Gemini API (free tier)
session 6 — LangChain Prompt templates, LCEL chains, runnables, structured output, memory, tools and agents, RAG basics, a leads agent LangChain, FAISS, Groq
session 8 — Production patterns Honest RAG that refuses when unsure; a self-correcting agent that fixes code until its tests pass Python, pytest
session 12 — Agent-to-agent (A2A) Agent cards, agents delegating to agents, an orchestrator, the official A2A SDK, a two-agent debate A2A protocol, Groq
ARGPT — Build a language model from scratch Tokenizer, transformer, training on a rented GPU, generation, serving it behind your own URL (~700 lines) PyTorch, Modal
WhatsApp bot A real WhatsApp bot answering messages with a LangChain chain Node.js, Baileys, LangChain.js, Groq

Quick start

git clone https://github.com/AbdulRahmanAzam/AI-Season-Course-Material.git
cd "AI-Season-Course-Material/session 3"      # pick any module
python -m venv .venv
# Windows: .venv\Scripts\activate   ·   macOS/Linux: source .venv/bin/activate
pip install -r requirements.txt
cp .env.example .env                           # add a free API key where the README asks

Then run the numbered files in order. Several modules fall back to a mock mode when no API key
is set, so you can follow the logic offline. Free API tiers are rate-limited — if you see
429 Too Many Requests, wait a minute and retry.

Suggested order for self-study

  1. Session 1 — get comfortable calling a model from code.
  2. session 3 — build RAG over your own PDFs (the most useful skill for real projects).
  3. Session 4 — write an agent loop by hand so frameworks stop being magic.
  4. session 6 — learn LangChain now that you know what it wraps.
  5. session 8 — make answers honest and agents self-correcting.
  6. session 5 and session 12 — extend agents to images, audio and documents, then to other agents.
  7. ARGPT — when you want to know what's inside a language model, build a small one.

Free guides

About AI Season

AI Season is a live online AI agents bootcamp founded in Karachi by Abdul Rahman Azam. The
6-week, 12-session course takes students with basic Python to building, evaluating and deploying AI
agents with LangChain, LangGraph, RAG, tool calling, MCP, guardrails and production deployment —
explained in Urdu, coded in English.

Website · Curriculum ·
Enrol · LinkedIn ·
Instagram

Yorumlar (0)

Sonuc bulunamadi