5 docs tagged with "Foundations"

The nine major machine learning techniques, the question each one answers, and how to map real-world business problems to algorithms.

The paradigm with no dataset — an agent learns by acting, receiving rewards and penalties, and navigating the exploration-exploitation tradeoff that decides whether it finds the global optimum or settles for a mediocre trap.

The five Python libraries that do the work, how they stack, scikit-learn's fit/predict/transform contract, and the seven-step pipeline every project follows.

Features, numeric vs categorical subtypes, the positive class convention, and the four learning paradigms - supervised, unsupervised, semi-supervised, and self-supervised.

Learning without being explicitly programmed - why rule-based systems collapsed, the paradigm inversion, Mitchell's T-P-E framework, and AI vs ML vs DL taxonomy.