Bias and Variance for Machine Learning

The text discusses two fundamental concepts in data science - bias and variance, which are often misunderstood even by seasoned data scientists. This video aims to provide clear definitions of these terms and their implications on model performance. High bias leads to underfitting, while high variance results in overfitting. Addressing these issues is crucial for improving model accuracy. The text also touches upon the concept of bias-variance trade-off and how it has become less significant nowadays.