Machine learning algorithms have emerged as a potent tool for COVID-19 data analysis, enabling researchers to identify patterns, predict outcomes, and optimize interventions. The COVID-19 pandemic has led to abundant data from diverse sources, including health organizations, governments, and individuals. This chapter explores the machine learning algorithms for COVID-19 data in Python. An overview of common machine learning algorithms, such as decision trees, random forests, support vector machine, and linear regression, and their applications for COVID-19 data analysis is discussed. Moreover, implementing these algorithms using Python libraries such as scikit-learn is explored. The findings demonstrate that machine learning algorithms can be practical for COVID-19 data analysis, providing insights into the spread of the virus and informing public health interventions.