Training and Building Machine Learning Models

This post details the process of training and building ML models, expanding on the foundational concepts of data labeling. It covers various data types (tabular, text, images), explains ML model architecture (weights, layers), the iterative training process to minimize loss, and the importance of data splitting (train, validation, test) for generalization. It also discusses tokenization for text, data scaling, ML modeling frameworks, and choosing appropriate evaluation metrics.