What you will learn

• Describe and quantify the uncertainty inherent in predictions made by machine learning models

• Visually and intuitively understand the properties of commonly used probability distributions in machine learning and data science

• Apply common statistical methods like maximum likelihood estimation (MLE) and maximum a priori estimation (MAP) to machine learning problems

• Assess the performance of machine learning models using interval estimates and margin of errors