Machine learning continues to evolve, presenting two distinct approaches: traditional and adaptive. Traditional ML relies on static algorithms, while adaptive machine learning dynamically adjusts to new data, offering unparalleled flexibility and learning efficiency. This post explores the key differences that set these methodologies apart, enhancing our understanding of AI's potential.