Earning Criteria

• The earner of this badge has successfully completed the miniproject associated with the regression module of the Practical Machine Learning course, scoring at least 90% on each question. They utilized Python’s scikit-learn library and used its standard scaler transformer and linear regression predictor in a pipeline to build a regression model predicting home values. They improved the model by performing feature engineering while keeping the model complexity low by implementing regularization.
• The earner of this badge has successfully completed the miniproject associated with the classification module of the Practical Machine Learning course, scoring at least 90% on each question. They have shown the ability to use Python’s scikit-learn library to build logistic regression models, make use of categorical features, evaluate models, tune hyperparameters, and prevent overfitting. They are able to build classification models on real-world data and draw insightful conclusions.
• The earner of this badge has successfully completed the miniproject associated with the unsupervised learning module of the Practical Machine Learning course, scoring at least 90%. They worked with data describing customers of a credit card company and used unsupervised learning techniques to segment customers into clusters. They have the ability to use Python’s scikit-learn library to perform dimensionality reduction and fit a clustering model on real-world data and draw insightful conclusions.