ABSTRACT
Machine learning involves the design and implementation of algorithms that learn from data and improve their performance through experience. These algorithms employ mathematical and statistical techniques to discover patterns, generalize from examples, and make accurate predictions or decisions. It involves the study of statistical models and algorithms that allow computers to analyze and interpret complex data patterns, learn from them, and make data-driven predictions or decisions. This ability to learn from data makes machine learning particularly useful for solving complex problems and making predictions in domains such as image and speech recognition, natural language processing, recommender systems, fraud detection, autonomous vehicles, and many more. Machine learning algorithms can be broadly categorized into three main types: supervised learning, unsupervised learning, and reinforcement learning.
