Todays episode we introduce you to machine learning models that have prediction errors, and these prediction errors are usually known as Bias and Variance. In machine learning, there will always be a deviation between the model predictions and actual predictions. The main aim of ML/data scientists is to reduce these errors in order to get more accurate results. In this episode we are going to discuss bias and variance, Bias-variance trade-off, Underfitting and Overfitting. Also, we would take a quick look on how AWS Sagemaker clarify helps us to understand data and model bias