Study Plan¶
Use this page in one of two ways:
- if you are learning AI for the first time, follow the phases in order
- if you are preparing for IOAI, use the phases as a gap map and start at the first one that still feels weak
For route-specific entry, use Beginner Path or IOAI Path.
Default Entry By Route¶
Use the route pages once, then use this page as the map:
- beginner route: start at Phase 1, then move forward in order
- IOAI route: start at Phase 2 if validation is weak, or Phase 6 if timed decision-making is the main weakness
- mixed case: if you can compare models but still mishandle tables, repair Phase 1 first and then jump back to your weak phase
Before Phase 1¶
You should already be able to:
- create and use a Python virtual environment
- run one script from the terminal
- read a short traceback
- tell the difference between a topic page, an example, and a track
If that is not true yet, start with Getting Started.
First 90 Minutes¶
Do this before you worry about the full plan:
- read Getting Started
- choose Beginner Path or IOAI Path
- run one small example from Examples
- inspect one artifact carefully
- write one short note about the next move
If you are on the IOAI route, do not keep re-choosing the route after this page. Use the phases below as a repair map and enter at the weakest layer.
Phase 1: Tooling And Inspection¶
Goal: Make arrays, tables, grouped summaries, and plotting feel mechanical enough that the first model does not break your attention.
Start with:
Before moving on, you should be able to:
- explain row versus column operations
- inspect missing values and label balance quickly
- build a simple feature matrix without losing row alignment
- make one plot that changes what you inspect next
Phase 2: Honest Validation¶
Goal: Make split discipline, baselines, cross-validation, tuning, calibration, and leakage checks feel non-negotiable.
Continue with:
Before moving on, you should be able to:
- keep train, validation, and test roles separate
- compare a learned model to a simple baseline honestly
- explain when cross-validation helps and when it does not
- describe one leakage pattern that could fake progress
Phase 3: Classical Model Choice¶
Goal: Add a few classical patterns that sharpen geometry, clustering, and representation judgment without losing evaluation discipline.
Continue with:
Before moving on, you should be able to:
- justify linear versus nonlinear boundaries
- explain what a low-dimensional view can and cannot prove
- say which clustering result is stable enough to trust
Phase 4: Deep Learning Mechanics¶
Goal: Make training loops, checkpoints, optimizer choices, and overfitting checks feel controlled instead of mysterious.
Continue with:
Before moving on, you should be able to:
- describe the difference between training and evaluation mode
- explain what the best checkpoint means in your workflow
- name one intervention for overfitting and one for unstable optimization
Phase 5: Transfer And Representation Reuse¶
Goal: Learn when to freeze, probe, fine-tune, or switch representation families instead of retraining everything from scratch.
Continue with:
Before moving on, you should be able to:
- justify frozen features versus fine-tuning
- compare at least two representation choices on one task
- say what evidence would make you stop adding model complexity
Phase 6: Decisions Under Constraint¶
Goal: Practice model choice, operating points, and workflow discipline under time, budget, or leaderboard pressure.
Continue with:
Before moving on, you should be able to:
- defend a stop-or-continue call
- choose a threshold or review budget policy explicitly
- explain why a public gain or small validation jump may still be untrustworthy
Suggested Weekly Rhythm¶
For steady progress, one week should usually include:
- one topic page
- one example run
- one track session
- one clinic or question pack
- one short written note about the weakest point in the workflow
That rhythm is better than reading many pages without producing evidence.