Building real competence.
WHAT THE COMMON MISTAKE IS
Learning frameworks before fundamentals.
WHY THAT FAILS
You can run a model without understanding what it does, and cannot diagnose it when it fails.
WHAT TO BUILD FIRST
Statistics and probability Linear algebra Enough calculus to understand gradients Programming competence
WHAT TO LEARN SECOND
Supervised learning fundamentals Evaluation, thoroughly Classical algorithms
WHY EVALUATION EARLY
It is where beginners most often go wrong, and the errors are invisible.
WHAT TO LEARN THIRD
Deep learning, if the work requires it.
WHY THAT ORDER
Most commercial problems are solved by classical methods on tabular data.
WHAT TO DO ALONGSIDE
Work on real, messy data.
WHY
Curated teaching datasets omit every difficulty that dominates real work.
WHAT TO PRACTISE DELIBERATELY
Framing a problem from a business need Finding leakage Explaining results to non-specialists
WHAT TO AVOID
Following tutorials without understanding Collecting certificates without building anything Chasing the newest technique
WHAT SIGNALS REAL LEARNING
Being able to explain why something did not work.
