A 17-course path to building AI locally
Fundamentals, local AI systems, RAG, agents, and MLOps.
19 structured courses and 356+ hands-on chapters. Start with “what is AI,” then build toward RAG apps, AI agents, local workflows, and production ML practices. The first chapter of every course are free.
Choose your path
What is AI
Understand every AI concept — from neural networks to production deployment
AI: Beginning to Advanced
Build neural networks from scratch and deploy production AI systems
Natural Language Processing
Build chatbots, search engines, and text analysis pipelines
Computer Vision
Create image classifiers, object detectors, and face recognition systems
RAG Systems
Build AI that answers from your private documents — zero data leaves your machine
Agentic AI
Design AI agents that use tools, plan, and solve complex problems autonomously
Multimodal AI Systems
Build systems that understand text, images, and audio together
MLOps
Deploy, monitor, and scale ML models like a senior ML engineer
Test-Time Compute Scaling
Make AI 10x cheaper to run in production with inference optimization
Reinforcement Learning
Train AI from human feedback — the technique behind ChatGPT
MCP Servers and Tool Ecosystems
Build, secure, and ship Model Context Protocol servers. Tools, resources, transport, observability, and production deployment patterns.
Voice AI and Realtime Agents
Speech-to-text, text-to-speech, realtime LLM orchestration, voice agents with tools, observability, and telephony deployment.
AI Engineering: From Prompt to Production
End-to-end AI app craft. Prompting, embeddings, tools, evals, guardrails, performance, deployment, and CI/CD for AI systems.
AI Security, Guardrails, and Red Teaming
Threat modeling, prompt injection, agent risks, data security, red teaming, monitoring, and compliance for production AI.
Human-AI Collaboration
Decision rights, trust calibration, oversight, workflow design, and team adoption for working alongside AI systems.
Local AI Deployment: From Laptop to Production
Real production code for deploying LLMs locally. Quantization, KV cache, vLLM, multi-GPU, edge devices, OpenAI-compatible servers. Full GitHub repo included.
Dataset Engineering: Build the Data That Makes Models Great
The discipline behind every great AI system. Collection, cleaning, deduplication at scale, synthetic data, instruction tuning, preference data, evals, contamination defense. Full GitHub repo with production code.
AI for Coding, Code Agents, and Engineering Workflows
Coming soon — Lifetime members get the launch + 30 days exclusive access.
Fine-Tuning, Distillation, and Model Adaptation
Coming soon — Lifetime members get the launch + 30 days exclusive access.
Tool kits are bonuses, not the main offer.
Paid plans include the Local AI kit bundle: prompts, Docker templates, RAG starter files, agent examples, automation scripts, and fine-tuning resources. You can also request the kits free after subscribing to both YouTube channels.
What you'll build
RAG Chatbot
AI that answers from your private documents
AI Agent
Autonomous agent that plans, uses tools, learns
Image Classifier
CNN for medical imaging, product detection
NLP Pipeline
Sentiment analysis, text generation, search
ML Pipeline
CI/CD, monitoring, A/B testing for models
RLHF System
Train AI from human feedback — like ChatGPT