Linear regression implementation in scikit-learn & evaluation metrics: R2 score, explained variance
37:56
Linear regression fundamentals: loss function, gradient, and optimization with Python example
29:51
Bias-Variance Tradeoff in Machine Learning and Regularization (L1 vs L2)
25:27
Empirical Risk Minimization and General Machine Learning Workflow
14:54
Feature Engineering Using The sklearn.preprocessing Package
36:05
Structure from Motion (SfM): From COLMAP to VGGSfM
10:35
Multiple Linear Regression in Python - sklearn
32:36
Data Loading, Understanding, and Filtering for Machine Learning Using Pandas
38:29