This chapter dives into the fundamentals of machine learning, exploring its brief history, underlying logic, major types, popular algorithms, and both advantages and limitations. The important topic of measuring uncertainty in data science is addressed, with methods such as the confusion matrix, receiver operating characteristic curve, and Monte Carlo simulations, which help assess model accuracy and performance. Strategies for reducing uncertainty are also included. Forecasting methods are examined, covering definitions, types, algorithms, applications, and the benefits and drawbacks of using these techniques. The next section explores forecasting with Japanese cherry blossom datasets and building effective storytelling, an essential skill in data science.