ML: Build, Train, Justify Models

Overview

ML: Build, Train, Justify Models gives learners a practical, end-to-end experience in turning real business problems into well-framed machine learning tasks, training multiple model families, and justifying model choices using bias–variance reasoning. Through short videos, hands-on exercises, and a Coursera Lab environment, learners practice reading product specifications, identifying the correct ML task, and building reproducible modeling workflows with APIs and experiment tracking. They train logistic regression, random forest, and gradient boosting models on tabular data, compare model behavior across repeated splits, and learn how to write clear, evidence-based recommendations. By the end, learners can confidently map business needs to ML tasks, train and evaluate diverse algorithms, and select models based on stability, interpretability, and performance rather than guesswork.

Syllabus

ML: Build, Train, Justify Models

ML: Build, Train, Justify Models gives learners a practical, end-to-end experience in turning real business problems into well-framed machine learning tasks, training multiple model families, and justifying model choices using bias–variance reasoning. Through short videos, hands-on exercises, and a Coursera Lab environment, learners practice reading product specifications, identifying the correct ML task, and building reproducible modeling workflows with APIs and experiment tracking. They train logistic regression, random forest, and gradient boosting models on tabular data, compare model behavior across repeated splits, and learn how to write clear, evidence-based recommendations. By the end, learners can confidently map business needs to ML tasks, train and evaluate diverse algorithms, and select models based on stability, interpretability, and performance rather than guesswork.