Your Learning Journey
Track your progress from AI foundations to frontier research.
0
of 41 modules completed
0
days in a row
7
Beginner to Expert
Level 0: AI Foundations
Understand what AI is, why it matters, and how to start using it today. No code, no math, no prerequisites.
History from Turing to GPT. Types of AI: narrow vs general vs super. The AI timeline and key milestones.
Mental models for understanding neural networks, training, and inference, no math required.
Map of major players, model families, and the open source vs closed source debate.
Hands-on: using ChatGPT, Claude, Gemini, and Perplexity. Prompt basics and platform comparison.
Bias, hallucinations, misinformation, privacy, and the alignment problem explained simply.
Industry-specific AI applications: healthcare, finance, legal, marketing, education, and more.
Level 1: AI Power User
Become a highly effective AI user. Master prompting, workflows, and tool selection. No coding required.
The CRAFT framework. Zero-shot, few-shot, and chain-of-thought prompting techniques.
Tree of thought, self-consistency, meta-prompting, prompt chaining, and structured outputs.
Deep dives into Claude Projects, ChatGPT, Notion AI, Perplexity, Gamma, and more.
Writing, editing, summarizing. Voice control, fact-checking, and human-in-the-loop editing.
Using AI tools to analyze spreadsheets, PDFs, and images without writing code.
Midjourney V7, GPT Image, Stable Diffusion, FLUX, Runway, Sora 2, ElevenLabs v3, and creative workflows.
Zapier AI, Make.com, n8n workflows. Connecting AI to your existing tools.
Level 2: Technical Foundations
Build the programming and mathematical literacy needed to understand AI at a deeper level.
Python fundamentals, NumPy, Pandas, Matplotlib. Enough to follow ML tutorials.
Linear algebra, calculus, probability: intuitive visual explanations, no proofs.
Data types, cleaning, preprocessing, feature engineering, and data pipelines.
Supervised vs unsupervised learning. Regression, classification, clustering with scikit-learn.
Neural networks, backpropagation, gradient descent. PyTorch introduction.
REST APIs, authentication, OpenAI API, Anthropic API, HuggingFace API.
Level 3: AI Builder
Build real AI-powered applications. Integrate LLMs, embeddings, RAG, and agents into production systems.
Transformer architecture, attention mechanisms, tokenization, context windows, and scaling laws.
Embeddings, vector databases, document chunking, and building a RAG pipeline from scratch.
Tool use, function calling, multi-step reasoning. LangChain, LangGraph, CrewAI, Autogen.
When to fine-tune vs RAG. LoRA, QLoRA, full fine-tuning. Training data and evaluation.
Chatbots, copilots, content generators, classification, recommendation, and search systems.
Anthropic's MCP standard for connecting AI to external tools and data sources.
Cursor, GitHub Copilot, Claude Code, Windsurf. AI pair programming and the AI-native dev lifecycle.
Level 4: AI Engineering
Production-grade AI systems. MLOps, evaluation, observability, and scaling.
Model serving, containerization, Kubernetes for ML, CI/CD, and cost optimization.
Benchmarks, evals, metrics for LLMs. Human evaluation, A/B testing, regression testing.
Logging, tracing agent workflows, monitoring latency/cost/quality. LangSmith, W&B, Arize.
Prompt injection, data privacy, PII handling, GDPR compliance, red teaming AI systems.
Caching strategies, rate limiting, multi-model routing, cost vs quality tradeoffs.
Level 5: AI Strategy & Leadership
Drive AI transformation at the organizational level. For founders, CTOs, and enterprise leaders.
Assessing AI readiness, building roadmaps, buy vs build, vendor evaluation, ROI modeling.
A2A protocols, agent identity infrastructure, business-to-agent interaction patterns.
Responsible AI frameworks, regulatory landscape, EU AI Act, internal AI policies.
Hiring, upskilling, center of excellence models, change management, AI OKRs.
AI-native vs AI-enhanced companies. Platform plays, vertical SaaS, economics of intelligence.
Level 6: Frontier AI & Research
Stay at the cutting edge. For researchers, advanced engineers, and AI enthusiasts tracking the frontier.
How to read arXiv papers, key conferences, paper dissection methodology.
Vision-language models, audio, video generation, embodied AI, and robotics.
Chain-of-thought, reasoning models, Monte Carlo Tree Search, the frontier of AI reasoning.
Llama, Mistral, Qwen. Running local models, quantization, open source vs proprietary.
What AGI means, capability gaps, scaling hypothesis, AI safety, alignment approaches.