Learning Track
Artificial Intelligence
From fundamentals to shipping AI features in production.
Understand what AI is, how machine learning works, neural networks, LLMs, the model landscape, Hugging Face open-source models, prompt engineering, building with the Anthropic and OpenAI APIs, agentic systems, evals, AI product design, and a deployed Anthropic + Next.js passion project. Earn the Artificial Intelligence certification.
Lessons
What is Artificial Intelligence?
Understand what AI actually is, its history, and where it's heading.
Machine Learning Fundamentals
Understand how machines learn patterns from data without being explicitly programmed.
Neural Networks and Deep Learning
Understand the building blocks of modern AI: neural networks, backpropagation, and deep learning.
What are Large Language Models?
Understand how LLMs work: tokenization, the transformer architecture, context windows, and why they seem intelligent.
The LLM Landscape: Models and Companies
Know frontier models (Claude, GPT-4o, Gemini), open-source (Llama, Mistral), multimodal capabilities, context windows, and how to pick for a use case.
Hands-on: Hugging Face and Open-Source Models
Use Hugging Face to find, evaluate, and run an open-source model and decide where open fits alongside frontier APIs.
Training and Fine-Tuning LLMs
Understand how LLMs are trained, aligned with RLHF, and customized using fine-tuning and LoRA.
Prompt Engineering
Learn to communicate effectively with AI models using zero-shot, few-shot, and chain-of-thought techniques.
Building with AI APIs
Use AI APIs to build applications: send requests, stream responses, and manage tokens and costs.
AI Agents and Agentic Systems
Understand AI agents that plan and act using tool calling, agent frameworks, and the Model Context Protocol.
AI Safety, Alignment, and Prompt Injection
Reason about bias, hallucinations, alignment, and the technical attack surface (jailbreaks, direct/indirect prompt injection).
Building AI Evaluations
Measure AI output quality, detect hallucinations, build a 20-case eval set, and operate evals as part of every change.
AI Product Development
Scope, cost, latency, UX, and the honest question of when AI is the wrong tool for the job.
AI Project: Build Something with AI
Apply everything learned by designing, building, testing, and presenting a real AI-powered project.
Cryptography Fundamentals for AI
Use hashing, encryption, and TLS to protect AI data and models, and understand federated learning, differential privacy, and homomorphic encryption.
Passion Project: Anthropic + Next.js + Vercel
Pick one of three project briefs and ship a deployed AI product end-to-end with the case study a hiring manager will read.
AI Resources and Next Steps
Know where to continue learning and stay current in the fast-moving AI field.
AI Engineer Job Readiness
Translate AI track skills into a resume, portfolio, and interview prep for AI engineer roles.