The primary components of machine learning include algorithms, data, and computational power. Machine learning algorithms provide the rules and processes for identifying patterns or making predictions based on data, while the data itself serves as the input source for training these algorithms. Computational power is necessary to process large datasets efficiently, enabling the algorithms to learn and improve over time. Together, these components facilitate the development of intelligent systems capable of adapting and performing tasks without explicit programming.