Beginner learning hub
Machine Learning Foundations
Machine learning foundations connect a decision to data, a measurable target, a baseline, a trained model, protected evaluation, and an operational decision. Learning algorithms without this complete workflow makes it difficult to tell whether a model is useful or merely complex.
Best for: Beginners and software developers starting applied machine learning
Recommended learning order
- 1AI & ML OrientationWhat AI and ML are, how learning differs from rules, and how to choose a first path.Open lesson
- 2Python & NumPy for MLWrite small Python programs and reason about arrays, shapes, vectorization, and reproducible experiments.Open lesson
- 3Data Exploration & PreprocessingInspect real data, handle missing values, encode features, avoid leakage, and build repeatable transformations.Open lesson
- 4ML Workflow & EvaluationFrame a problem, split data correctly, choose metrics, detect overfitting, and compare against a baseline.Open lesson
- 5Linear RegressionPredicting continuous values using a linear relationship.Open lesson
- 6Logistic RegressionBuild an interpretable binary classifier using logits, sigmoid probabilities, cross-entropy, and thresholds.Open lesson
- 7Responsible AI FoundationsTranslate fairness, privacy, safety, transparency, and human oversight into testable design decisions.Open lesson
Decisions this path should help you make
- When should a problem use deterministic rules instead of ML?
- Which split reproduces the deployment boundary?
- Which metric and threshold represent the real error costs?
- What evidence is required before a limited launch?