Who It's For & What You'll Master
The complete AI journey — from absolute beginner to building production autonomous agents — in 8 intensive weekends. No prior AI experience needed.
Total Beginners
Start from Python basics and progress all the way to deploying secure agentic AI systems — zero to production in 8 weeks.
Software Engineers
Add the full AI stack — ML, Gen AI, and agentic pipelines — to your engineering toolkit and ship AI-powered products.
Data Analysts
Upgrade from dashboards to ML models, RAG pipelines, and cloud MLOps deployments in a single comprehensive program.
Career Switchers
The most efficient path into AI engineering — structured, hands-on, and portfolio-building from day one.
Tech Professionals
Get serious AI skills fast. This program covers the breadth that matters — ML, LLMs, agents, cloud, and security.
Students & Graduates
Build 8 portfolio projects spanning the entire modern AI stack — from Scikit-Learn to LangGraph to Docker deployment.
8-Week Complete AI Journey
Three phases — ML Foundations, Generative AI, and Agentic Systems — each building on the last. Every week: Saturday (3 hrs) Training + Sunday (3 hrs) POC & Project.
🐍 Python for AI/ML — Data Foundations
☀️ SATURDAY | 3 Hours | TRAINING
Python essentials for AI — data types, loops, functions & list comprehensions
NumPy arrays, matrix operations & vectorized computation for ML
Pandas for data manipulation — DataFrames, cleaning, and feature prep
Matplotlib & Seaborn — visualizing data distributions and correlations
🌅 SUNDAY | 3 Hours | POC + PROJECT
⚒️ TOOLS
- Python 3.11+
- NumPy
- Pandas
- Matplotlib / Seaborn
- Jupyter / Google Colab
📅 SUMMARY
| Day 1: | Training + Demo |
| Day 2: | POC + Project |
| Output: | Data Report App |
🤖 ML Algorithms, Prompt Design & Model Evaluation
☀️ SATURDAY | 3 Hours | TRAINING
Supervised ML — linear regression, logistic regression & decision trees with Scikit-Learn
Model evaluation — accuracy, precision, recall, F1-score, ROC-AUC & confusion matrices
Context engineering — structuring system prompts, conversation history & retrieved context
Prompt engineering — zero-shot, few-shot, chain-of-thought & token budget management
🌅 SUNDAY | 3 Hours | POC + PROJECT
⚒️ TOOLS
- Scikit-Learn
- Python ML Pipeline
- OpenAI Playground
- Claude.ai / Gemini
- Jupyter Notebook
📅 SUMMARY
| Day 1: | Training + Demo |
| Day 2: | POC + Project |
| Output: | Credit Risk Model |
🧠 Deep Learning — Neural Networks with PyTorch
☀️ SATURDAY | 3 Hours | TRAINING
PyTorch fundamentals — tensors, autograd, computational graphs & GPU usage
Neural network architecture — layers, activations, loss functions & optimizers
Training loops, overfitting prevention — dropout, batch norm & early stopping
MLflow for experiment tracking — logging metrics, artifacts & model registry
🌅 SUNDAY | 3 Hours | POC + PROJECT
⚒️ TOOLS
- PyTorch
- MLflow
- CUDA / GPU Colab
- NumPy
- Matplotlib
📅 SUMMARY
| Day 1: | Training + Demo |
| Day 2: | POC + Project |
| Output: | Neural Net + Dashboard |
🔮 Transformer Architecture & Gen AI Fine-Tuning
☀️ SATURDAY | 3 Hours | TRAINING
Attention mechanism deep dive — self-attention, multi-head attention & positional encoding
Hugging Face Transformers — loading, fine-tuning & inference with pre-trained models
Advanced prompt engineering — system prompts, few-shot, chain-of-thought & structured output
Fine-tuning concepts — LoRA, PEFT overview, when to fine-tune vs. prompt-engineer
🌅 SUNDAY | 3 Hours | POC + PROJECT
⚒️ TOOLS
- HuggingFace Transformers
- PyTorch
- LoRA / PEFT
- Gradio
- WandB
📅 SUMMARY
| Day 1: | Training + Demo |
| Day 2: | POC + Project |
| Output: | Sentiment App |
☁️ Cloud Gen AI APIs — AWS, Azure & GCP
☀️ SATURDAY | 3 Hours | TRAINING
Calling AI APIs — OpenAI, Anthropic Claude & Google Gemini in Python with streaming
AWS Bedrock — invoking foundation models (Claude, Titan, Llama) from Python
Azure AI Services — Azure OpenAI, AI Language APIs & content safety
GCP Vertex AI — calling Gemini models & cloud ML pipeline intro
🌅 SUNDAY | 3 Hours | POC + PROJECT
⚒️ TOOLS
- OpenAI Python SDK
- Anthropic SDK
- AWS Bedrock
- Azure AI Studio
- GCP Vertex AI
📅 SUMMARY
| Day 1: | Training + Demo |
| Day 2: | POC + Project |
| Output: | AI Summarizer App |
📚 Vector Databases & RAG Pipelines
☀️ SATURDAY | 3 Hours | TRAINING
Embeddings — sentence transformers, text embeddings & semantic similarity search
Vector databases — Pinecone, ChromaDB & FAISS — indexing, querying & filtering
RAG architecture — retrieval-augmented generation end-to-end with LangChain
RAG evaluation with RAGAs — faithfulness, context relevance & answer quality metrics
🌅 SUNDAY | 3 Hours | POC + PROJECT
⚒️ TOOLS
- Pinecone
- ChromaDB
- LangChain
- RAGAs
- Sentence Transformers
📅 SUMMARY
| Day 1: | Training + Demo |
| Day 2: | POC + Project |
| Output: | Enterprise Q&A |
🦾 Agentic AI — LangGraph, Tools & Multi-Step Pipelines
☀️ SATURDAY | 3 Hours | TRAINING
LangGraph fundamentals — stateful graph-based agent orchestration & node design
Tool calling & function calling — OpenAI tools, Anthropic tool use & Gemini functions
Memory management — short-term, long-term (Mem0) & episodic memory for agents
Cloud Agentic AI — AWS Bedrock Agents, Azure AI Agent Service & Vertex AI Agents
🌅 SUNDAY | 3 Hours | POC + PROJECT
⚒️ TOOLS
- LangGraph
- Mem0
- Bedrock Agents
- Tavily Search
- FastAPI
📅 SUMMARY
| Day 1: | Training + Demo |
| Day 2: | POC + Project |
| Output: | Research Agent |
🛡️ AI Security, Guardrails & Production Capstone
☀️ SATURDAY | 3 Hours | TRAINING
Prompt security & OWASP Top 10 for LLMs — prompt injection, jailbreaking, insecure output & data poisoning
AI security anti-patterns — trusting raw LLM output, storing PII in prompts, over-privileged agents
AI security patterns — input sanitisation, output validation, sandboxed tools, audit logging & human-in-the-loop
Guardrail implementation — NeMo Guardrails, Llama Guard, PromptFoo red-teaming, Docker & CI/CD deployment
🌅 SUNDAY | 3 Hours | CAPSTONE
⚒️ TOOLS
- NeMo Guardrails
- Llama Guard
- PromptFoo
- LangSmith
- Docker / GitHub Actions
📅 SUMMARY
| Day 1: | Security Deep-Dive |
| Day 2: | Red-Team + Capstone |
| Output: | Secure Production App |
Eight Weeks, Eight Milestones
Each week delivers a tangible, portfolio-ready output — building toward a fully deployed, production-grade AI system by Week 8.
Python & Data Mastery
Comfortable with NumPy, Pandas & visualization — ready to tackle any ML dataset
ML Models & Prompting
Trained real classifiers and understand how today's LLMs work in theory and practice
Deep Learning Engineer
PyTorch neural networks trained, tracked with MLflow experiment dashboards
Transformer & Gen AI
Fine-tuned transformer model with Gradio UI deployed for live inference
Cloud AI Integration
Called 3 cloud AI platforms (AWS, Azure, GCP) from Python and built a working AI app
RAG Pipeline Builder
Enterprise document Q&A system with vector search, source citation & quality evaluation
Agentic AI Developer
Multi-tool LangGraph agent with persistent memory and autonomous task execution
Production AI Engineer
Fully deployed, secured, containerised AI application — portfolio-ready for interviews
The Complete Modern AI Stack
Every tool you'll touch is actively used by AI engineers at leading tech companies.
One Program. The Complete AI Journey.
Everything in Basic + Intermediate, combined and optimised into 8 action-packed weekends — at a significant saving.
- ✓48 hours of live weekend training (Sat + Sun)
- ✓8 hands-on POC + mini projects
- ✓3 phases: ML → Gen AI → Agentic AI
- ✓Cloud labs (AWS Bedrock, Azure AI, GCP Vertex)
- ✓Lifetime recording access
- ✓Private Discord community
- ✓Spairo Academy certificate
- ✓Priority upgrade discount to Advanced AI/ML
Frequently Asked Questions
Everything you need to know before joining the Zero to Agent Bootcamp.