Machine Learning Fundamentals

Machine learning can feel like a pile of library calls and buzzwords. You can run a model, but you are not sure why it works, why it fails, or whether the score you are looking at means anything.

Machine Learning Fundamentals builds intuition before it adds algorithms. You begin with the full machine learning workflow, then implement linear regression from scratch and move to classification with logistic regression. A chapter on evaluation, validation, and metrics teaches you how to judge a model honestly. From there you work through decision trees and rule-based models, ensembles and gradient boosting, support vector machines and distance-based models, unsupervised learning and clustering, and PCA for dimensionality reduction.

The final chapter brings everything together in full projects, with model selection and debugging.