Core ML Overview

Build solid ML skills step by step. Clear goals, small projects, and habits that make models reliable in the real world.

ML Lifecycle

From idea to impact

See the full journey: define the problem, prepare data, build a baseline, evaluate with the right metrics, deploy, and monitor. Learn to loop—measure, improve, and repeat.

You’ll learn: Framing problems, setting metrics, experiment tracking, deployment basics, and monitoring

Model foundations

The classics that still win

Master the workhorses: linear/logistic regression, trees, ensembles, and k‑NN. Understand bias–variance, regularization, cross‑validation, and when simple beats complex.

You’ll learn: Baselines, feature–target fit, regularization, cross‑validation, and strong starter models

Supervised learning

Predict with labels

Build classifiers and regressors that generalize. Compare models, tune hyperparameters, and read learning curves to avoid overfitting.

You’ll learn: Pipelines, metrics (accuracy, F1, ROC‑AUC, MAE/RMSE), hyperparameter search, and model selection

Unsupervised learning

Find structure without labels

Discover patterns and reduce noise. Cluster similar items and compress features while keeping signal.

You’ll learn: k‑means, hierarchical clustering, DBSCAN, PCA, t‑SNE/UMAP basics, and evaluation without labels

---

Start simple, measure honestly, and improve in small steps. A clear baseline plus good metrics will take most projects farther than fancy tricks.