Introduction to Machine Learning- Concepts, Algorithms, and Practical Applications

We live in the era of Big Data, where massive volumes of information are generated every second. Oneof the most transformative subareas of Artificial Intelligence (AI) that has emerged from reading,analyzing, and identifying complex patterns in data is Machine Learning. In this article, I present a briefhistorical context of the field, exploring its evolution from the first algorithms to modern architectures.Then, I examine the fundamental concepts that underpin the field, including the main paradigms andalgorithms of supervised and unsupervised learning, as well as their practical applications. I alsodiscuss the emergence of synthetic data as an innovative strategy to overcome limitations in theavailability and quality of real data, in addition to its ethical and technical implications.