Machine learning algorithms are different from conventional algorithms as they improve solutions by means of optimization using experience, i.e., multiple data points. This chapter introduces the foundational principles of machine learning, including data structures, types of learning, optimization techniques, and challenges such as overfitting and regularization. The discussion focuses on fundamental algorithms like linear regression and gradient descent, laying the groundwork for understanding their use in both deep learning and computational mechanics. These concepts are presented in a simple and accessible manner, catering to readers with no experience in machine learning.