NuthinButta Intelligence
Authored curriculum with independent evidence reviews
Machine Learning: Foundations to Research
A dependency graph of 29 core skill protocols and 11 optional specialization protocols with artifact-based manual mastery gates.
Difficulty
Advanced
Time / Day
60–90 minute practice sessions
Duration
Evidence-paced; no fixed completion date
Your path through machine learning
Begin with Scientific Python →29 core protocols, 1,135–1,940 active hours, plus at least one relevant specialization. The seven milestones below develop capabilities you can demonstrate along the way. Hours are estimates, never pass thresholds.
Start here: check your entry readiness
Before the foundations, you should be able to:
This checklist is a personal readiness check, not a placement test or a saved mastery decision. Fill gaps before the relevant foundations. Experienced learners may enter later and submit prior work against the same evidence criteria; nothing is automatically marked passed.
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Why This System Works
Advance by demonstrated mathematical, computational and experimental capability; revisit failed evidence instead of counting pages or elapsed days.
Expected Outcomes
- →Mathematical derivations agree with numerical checks and stated assumptions.
- →A reviewer reproduces model comparisons and can inspect negative results.
- →A bounded research question produces evidence and a response to external criticism.
System Rules
- 01Use prerequisites as recommended readiness; existing knowledge can be reviewed against the same evidence rubrics. There are no automatic entry locks.
- 02Every protocol requires all 4 assessment criteria; reading alone never passes.
- 03Always preserve configurations, seeds, raw results and failed attempts.
- 04Choose at least one specialization before the original research gate; do not take all branches by default.
- 05Never interpret an execution checkmark or activity badge as technical certification.