Choose a course
Start from the public catalog and pick the track that matches the outcome you want.
Open catalogPick a course, learn the concept, practice in a safe workspace, and keep proof of what you can explain. The beta catalog is live on the web with progress, notes, sandbox checks, and code practice built into the learning flow.
Learn Python from first script toward a portfolio progress tracker/reporting capstone. Each hosted room opens a browser Python sandbox, checks the required evidence, and connects the final proof to realistic assistant, trainee, and Python internship paths.
Start from the public catalog and pick the track that matches the outcome you want.
Open catalogUse guided rooms first, then open the standalone sandbox for focused experiments.
Open sandboxSave notes, Check Work results, and explanations that show what you understood.
Open workspaceThese are live course paths with learner-visible lessons. Open the catalog for the full room map, progress tracking, and practice state.
A beginner-to-intermediate Python path with browser practice, Check Work, debugging feedback, and a portfolio capstone: a progress tracker/reporting tool for realistic entry-level lanes like data/reporting, operations automation, support automation, QA, technical assistant work, and Python internships.
Build practical security fundamentals: classify threats, design layered controls, implement access protections, operate incidents, and explain risk decisions.
Learn authorized security assessment workflow: scope, reconnaissance, validation, evidence handling, reporting, remediation, and defender handoff.
Use Kali Linux as a controlled security lab workstation: prepare scope, collect evidence, document tools, and prove safe workflows before advanced testing.
Practice defensive security operations: triage alerts, read SIEM evidence, map threat intelligence, operate incidents, preserve evidence, and hunt for suspicious behavior.
Use Python for safe security automation: parse logs, model scoped network checks, handle HTTP evidence, reason about hashing, build defensive helpers, and validate risky input.
Build AI agents with clear goals, prompt contracts, retrieval boundaries, tool permissions, evaluation checks, and production runbooks.
Build practical AI application judgment through prompts, structured outputs, internal API-powered AI interaction, retrieval, workflows, guardrails, evaluation, and portfolio proof.
Build production-grade AI systems through data contracts, pipelines, feature plans, validation, deployment monitoring, and scaling runbooks.
Build Python web applications from request handling through APIs, databases, authentication, testing, and deployment.