Comparative analysis of machine learning methodologies and technologies

The paper deals with a comparative analysis of three key machine learning (ML) paradigms: supervised learning, unsupervised learning, and reinforcement learning – along with an evaluation of popular ML frameworks such as TensorFlow, PyTorch, and Scikit learn. The author considers the main differences, advantages and limitations of ML approaches, focusing on factors such as: computational cost, scalability and ease of imple mentation. The study looks at the interpretability aspects of ML models and analyses the computational resources required for their operation, including the load on the central pro cessing unit and random access memory. The results provide the necessary information on how different ML methodologies and technologies shape real applications and influence practical decision-making in systems driven by artificial intelligence.