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Route Guide

Beginner Path

Use this route if you are learning AI for the first time or if the data and evaluation basics still feel shaky. The goal is not to touch everything. The goal is to build a stable first workflow.

Getting Started Topics Examples First Track

Who It Is For

First Stable Workflow

Use this path when you can code a little but you still need arrays, tables, splits, and plotting to feel routine.

First Goal

Inspect Before Modeling

The first win is not a high score. The first win is knowing what the table looks like, what the split is, and what artifact you should inspect first.

Exit Rule

Move On Deliberately

Leave this route only when the first baseline, the split rule, and the weak slice note feel mechanical.

Use This Route If

  • this is your first serious AI workflow
  • terminal commands still require a little attention
  • NumPy, pandas, and plotting are not yet automatic
  • train, validation, and test roles still blur together

First 90 Minutes

  1. read Getting Started
  2. read Table Inspection
  3. run academy/.venv/bin/python academy/examples/numpy-shape-sanity/shape_sanity.py
  4. run academy/.venv/bin/python academy/examples/course-support-inspection/quick_inspection.py
  5. read Feature Matrix Construction
  6. write three lines:
  7. what the data looked like
  8. what could go wrong
  9. what you would inspect before modeling

First Week

Session 1: Array Shapes and Axis Operations and Table Inspection

Session 2: Grouped Summaries and Slice Checks and Plotting for Model Debugging

Session 3: Feature Matrix Construction and Honest Splits and Baselines

Session 4: Python, NumPy, Pandas, Visualization

Session 5: scikit-learn Validation and Tuning

Default First Track Sequence

Use this exact order if you do not want to improvise:

  1. finish the two starter examples from this page
  2. open Python, NumPy, Pandas, Visualization
  3. run academy/.venv/bin/python academy/labs/python-numpy-pandas-visualization/src/course_support_eda.py
  4. run academy/.venv/bin/python academy/labs/python-numpy-pandas-visualization/src/feature_matrix_walkthrough.py
  5. do Exercise Sets 1 and 2 in academy/exercises/python-numpy-pandas-visualization/
  6. do Exercise Sets 3 and 4 only after the lab baseline and feature-matrix path feel stable

What To Carry Forward

Keep one short note after each session with:

  • the baseline
  • the split rule
  • the first weak slice
  • the next move

That note matters more than reading extra pages.

You Are Ready To Leave This Route When

  • you can inspect a table quickly without guessing what to check
  • you can explain why the baseline exists before tuning
  • you can describe one leakage risk without prompting
  • you can finish a first track and say what artifact mattered most

When that is true, move into Study Plan and the wider Track Guide.