01
Start with the ML problem-solving loop
Understand features, targets, training examples, predictions, loss, train/test separation and evaluation. This gives every later algorithm a home.
MACHINE LEARNING ROADMAP • BEGINNER → PRACTITIONER
Learn ML in the order the ideas actually connect.
Do not memorize fifteen algorithms independently. Learn the shared mathematical ideas first, then see how each new model changes the hypothesis, loss, assumptions or optimization strategy.
RECOMMENDED ORDER
The shortest path to a connected ML mental model
FOUNDATION
02
Learn the minimum mathematics for ML
Focus on vectors, matrices, distance, probability, derivatives, gradients, variance and covariance. Learn each idea through the algorithms it powers rather than as isolated theory.
03
Regression: learn what “fitting a function” means
Begin with a straight line, add more features, bend the function, then control overfitting with regularization. This sequence makes bias, variance and optimization tangible.
04
Classification: learn different ways to draw a decision
Compare probability, neighbourhoods, Bayes, rules, ensembles and maximum-margin geometry. The models solve the same broad task using very different assumptions.
05
Unsupervised learning: discover structure without labels
See the difference between centroid-based, hierarchical and density-based clustering, then learn PCA as a variance-preserving change of coordinates.
06
Compare, select and justify
The final skill is not naming algorithms—it is choosing one for a data/problem context and explaining the trade-offs in accuracy, interpretability, speed, assumptions and complexity.
START HERE
Your first model: Linear Regression
It introduces prediction, parameters, residuals, loss, fitting and gradient descent in one visual problem.