Machine Learning Fundamentals · Foundations

Types of ML problems

The basic split between supervised (labeled examples), unsupervised (no labels), and reinforcement learning.

Also known as: supervised vs unsupervised, classification vs regression.

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Leads to

• Linear regression: Fitting a straight line (or hyperplane) through data to predict a continuous number.
• Train/test/validation split: Holding out data the model never trains on, so you can honestly check how it does on unseen examples.
• Decision trees: A model that predicts by asking a sequence of yes/no questions about the input, like a flowchart.
• K-nearest neighbors: Classifying a new point by looking at whatever its closest labeled neighbors are.
• Naive Bayes: A probabilistic classifier that (naively) assumes every feature is independent of the others given the class.
• Feature engineering and scaling: Reshaping raw data (scaling numbers, encoding categories) into a form a model can actually learn from well.
• K-means clustering: Grouping unlabeled points into k clusters by repeatedly assigning them to the nearest center and recomputing centers.

Related topics

• Decision trees: A model that predicts by asking a sequence of yes/no questions about the input, like a flowchart.
• K-nearest neighbors: Classifying a new point by looking at whatever its closest labeled neighbors are.
• K-means clustering: Grouping unlabeled points into k clusters by repeatedly assigning them to the nearest center and recomputing centers.