Online Courses

Self-paced courses with video lectures, hands-on projects, and comprehensive learning paths.

Audience

Level

✎You

◆Agent

✉Email

📅Calendar

📄Files

★Skills

💬Slack

⏱Schedule

✔Done

Build Your Personal AI Agent Assistant

All LevelsComing Soon

AgentsPersonal assistant

Learn to build your own personal AI agent that manages email, calendar, files, and workflows. No programming experience required — configure Claude Code and Cowork with MCP integrations, custom skills, and automation to work for you daily.

Coming Soon

> make an AI agentclass Agent: def run(self, task): return self.think(task)

AI-Powered Development

All LevelsComing Soon

CodingDevelopmentAgentsFullstackDevOps

Learn to build and ship your own AI agent chatbot interface using agentic AI coding tools. From first mockup to deployed product — master prompting, spec-driven design, databases, auth, and CI/CD with AI as your co-pilot.

Coming Soon

▸Prompt

◆Agent

◫RAG

◓Memory

⌕Search

</>Code

⇄API

⚙Planner

✔Output

Building AI Agents with Python

ADVANCEDComing Soon

CodingPythonLangChainAgents

Understand generative AI, NLP, prompting techniques, and how to build intelligent AI agents.

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from sklearn.ensemble import RandomForestClassifierclf = RandomForestClassifier()clf.fit(X_train, y_train)clf.score(X_test, y_test)▸ 0.94

Applied Machine Learning

BEGINNERComing Soon

CodingPythonMachine Learning

A practical introduction to machine learning: Python environments, core ML concepts, and hands-on projects.

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β = (XᵀX)⁻¹Xᵀyθ = θ − α∇J(θ)σ(x) = 1 / (1 + e⁻ˣ)L = ¹⁄ₙ Σ(ŷ − y)²m = β₁m + (1−β₁)ga · b = Σ aᵢbᵢD = √((x−μ)ᵀΣ⁻¹(x−μ))P(y) = eᶻ / ΣeᶻH = −Σ y log(ŷ)P(A|B) = P(B|A)P(A) / P(B)G = 1 − Σ pᵢ²J + λ‖w‖²

Foundational Machine Learning

ADVANCEDComing Soon

CodingPythonFundamentalMachine Learning

50+ video hours, 6+ projects covering ~50 ML topics. From data cleaning to model explainability.

Coming Soon