Naive Bayes explained simply: Bayes’ theorem, the naive assumption, a worked spam example, types, Laplace smoothing, Python code and real uses.

Support Vector Machine explained simply: hyperplane, margin, support vectors, the kernel trick, C and gamma, a worked example, Python code and applications.

K-Nearest Neighbours explained simply: how KNN works, distance metrics, a worked example, choosing k, feature scaling, Python code, pros, cons and applications.

Logistic regression explained simply: the sigmoid, odds and log-odds, a worked example, why we use it, Python code, odds ratios, metrics and limitations.

Linear regression explained simply: the equation, how the best-fit line is found, a worked example, why and how to use it, Python code, metrics and assumptions.

Supervised machine learning explained: classification vs regression, key algorithms, evaluation metrics, overfitting, a Python example, applications and how to learn it.

Unsupervised machine learning explained: types, algorithms (K-means, DBSCAN, PCA, Apriori), evaluation, a Python example, real applications and how to learn it.