Applied Machine Learning: Features and Models

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Introduces applied machine learning concepts such as data collection, feature engineering, model selection, and performance evaluation metrics.

Delves into the significance of features, model evolution, labeling challenges, and model selection in machine learning.

Introduces machine learning basics, including data collection, model evaluation, and feature normalization.

Covers model complexity, overfitting, and strategies to select appropriate machine learning models.

Explores model assessment metrics, selection techniques, bias-variance tradeoff, and handling skewed data distributions in machine learning.

Explores the MODNet methodology for material property predictions, emphasizing feature selection and supervised learning.

Discusses decision trees and random forests, focusing on their structure, optimization, and application in regression and classification tasks.

Explores image classification using decision trees and random forests to reduce variance and improve model robustness.

Explores the challenges and insights of deep learning, focusing on loss landscape, generalization, and feature learning.

Explores model selection criteria like AIC, BIC, and Cp in statistics for data science.