Train-Test Split and Cross-Validation, sklearn.model_selection, cross_val_score, and cross_validate
14:54
Feature Engineering Using The sklearn.preprocessing Package
25:27
Empirical Risk Minimization and General Machine Learning Workflow
37:56
Linear regression fundamentals: loss function, gradient, and optimization with Python example
6:05
Machine Learning Fundamentals: Cross Validation
14:45
Application of Calculus in Backpropagation
2:13
Intuition: Training Set vs. Test Set vs. Validation Set
32:36
Data Loading, Understanding, and Filtering for Machine Learning Using Pandas
38:29