HyperSense AI Studio-Be a citizen data scientist-Expert 101

Master common machine learning techniques, including regression, classification, clustering, and association analysis, and learn how models learn from data; evaluate performance on unseen data; explore ensemble methods.

Explore how algorithms learn from data through supervised learning in regression, classification, clustering, using gradient descent to optimize representations and hypothesis spaces, evaluate with accuracy, precision, recall, RMSE, and silhouette.

Evaluate machine learning models using classification metrics like confusion matrix, accuracy, precision, recall, f1, roc-auc, and regression metrics such as rmse and mae, plus clustering metrics such as silhouette score.

Improve models by adding data, crafting features, and ensuring diversity; tune hyperparameters with grid, random, and Bayesian methods for random forest, balancing accuracy and efficiency.

Explore auto cash, a capability that combines algorithm selection with barometer optimization for classification and regression, returning the best model and hyper barometer fit on your training data.

Explore why mortgage predictions occur and how credit scoring uses explainable and black box models, applying local, global, and counterfactual explanations for fairness and improvement.

Explore a campaign management use case in HyperSense AI Studio, identifying target customers, tailoring offers, and selecting communication channels for personalized retention, upselling, and cross-selling.

Thank you for joining HyperSense AI Studio to become a citizen data scientist. Please share your feedback to improve civics-focused content and stay safe and take care.