Python, properly
Syntax, functions, data structures, modules, files, errors, and object-oriented thinking. Build a small utility before adding another course.
Weeks 1–8 · Notebook + CLI
Build the skills. Prove the work.
A weekly runway from “starting from scratch” to a credible AI and Machine Learning internship portfolio by the end of 2026. The target stays ambitious. The next move stays concrete.
Set your runway
Target: ₹1,00,000/month AI + ML internship
This week's runway
0%
proof complete4hPython
3hLinear algebra
2hML practice
1hPublic proof
A curriculum that closes with proof.
Foundations are not a waiting room. Each chapter ends in something another person can inspect.
Original commitment
Keep the public target in view while the weekly work remains specific.
Source roadmapLinear algebra
Vectors, matrices, transformations, eigenvectors, and the geometry beneath optimization.
Weeks 4–12 · Visual explainerML core
Implement regression and classification, then explain metrics and failure modes in your own words.
Weeks 10–22 · Reproducible experimentsPortfolio signal
Two end-to-end projects with clean READMEs, measurable decisions, and a short technical walkthrough.
Weeks 18–36 · Public proofAttach the evidence.
A checked box says “done.” A URL says what you built, how it behaves, and what you learned.
Learn in public, but measure in artifacts: code, experiments, explanations, and reliable weekly follow-through.
Your ambition is annual. Your evidence is weekly.
Revise the week
The export includes your target, allocation, milestones, evidence links, progress, and next commitment. No account required.
Weekly Runway Allocation and Proof Engine
Read the explanation
Your weekly learning runway transforms hours and skill levels into an explicit breakdown across Python, linear algebra, machine learning, and public proof. Adjusting skill options shifts the underlying weight matrix: as you master foundations, Python and math hours decrease, surging your weekly machine learning time. Checking milestones updates your verified progress score and embeds inspected proof links directly into an exportable roadmap bundle.