Pick your goal. Follow the path. Build real skills with curated free resources.
Python → ML → Deep Learning → LLMs → Production deployment. The complete 6-month path from beginner to job-ready AI engineer.
3-month fast track. APIs → Prompting → RAG → Agents.
Stats → Pandas → Scikit-learn → MLOps. Full curriculum.
Beginner-friendly. No coding required for level 1.
Math → Papers → PyTorch → Original research.
For PMs adding AI features. No code, deep concepts.
Build production AI applications. From Python fundamentals to deployed LLM systems.
Variables, loops, functions, classes, virtual environments.
Linear algebra, vectors, matrices, basic calculus, probability — only what you need.
Data manipulation, vectorized operations, dataframes.
Git, GitHub, VS Code, Jupyter, command line basics.
Regression, classification, clustering, train/test split, cross-validation.
Neural networks from scratch, backpropagation, PyTorch fundamentals.
Image classifier (CNN) and text sentiment analyzer (RNN).
Attention mechanism, transformer architecture, OpenAI API basics.
System prompts, chain of thought, retrieval-augmented generation with LangChain or LlamaIndex.
Run Gemma, Llama, Mistral with Ollama. Quantization basics.
Build agents with LangGraph, CrewAI, or AutoGen. MCP integration.
FastAPI + Docker. Deploy to Vercel, Railway, or AWS.
Build something real — RAG chatbot for a domain, AI agent that does a useful task, etc.
GitHub README, blog post, demo video. Apply to 50+ jobs. Visit our Career page.
For developers who already code. Skip the basics, go straight to building AI apps.
Authentication, streaming, function calling, structured outputs.
System prompts, few-shot, chain of thought, evaluations.
OpenAI embeddings, Chroma or Pinecone basics.
Run Gemma, Llama, Mistral. Quantization, GPU vs CPU.
LangGraph, CrewAI, AutoGen. Pick one and ship.
Connect agents to your tools and data.
Build, deploy, write about it. Done.
From statistics to production ML systems.
Mean, median, variance, distributions, correlation.
Hypothesis testing, p-values, confidence intervals, A/B testing.
Bayes' theorem, conditional probability, common distributions.
Joins, window functions, CTEs. You'll use this daily.
DataFrames, groupby, vectorized operations.
Matplotlib, Seaborn, Plotly.
Linear/logistic regression, trees, ensembles, cross-validation.
PyTorch basics, transfer learning.
MLflow, model versioning, deployment with FastAPI + Docker.
Master the art of getting LLMs to do exactly what you want.
For those who want to push the frontier.
Eigenvalues, SVD, matrix calculus.
Bayesian inference, information theory.
Gradient descent variants, convex optimization.
Attention Is All You Need, BERT, GPT, ResNet, AlphaGo.
arXiv-sanity, Papers With Code, AK on Twitter.
Add AI features confidently. No coding required.