Machine Learning (ML)

Machine Learning (ML) is a type of Artificial Intelligence (AI) that allows software applications to perform specific tasks without explicit instructions. It focuses on the development of computer programs that can change and improve when exposed to new data.

ML algorithms are used to solve problems such as medical diagnosis, speech and image recognition, sorting and cataloging items, autonomous robots, fraud detection, predictive analytics, autonomous navigation, and natural language processing. The algorithms are usually run on data taken from a variety of sources such as surveys, observations, and experiments.

ML algorithms utilize a wide range of techniques, such as supervised and unsupervised learning, as well as deep learning. Supervised learning is when a system is provided with the data and a predetermined response it should provide, while the unsupervised learning allows the system to learn from data without a task pre-determined by the system. Deep learning is a subset of ML and uses larger input data and larger networks of neurons so that the system can learn complex tasks.

ML has a wide range of applications, from industries like healthcare, cybersecurity, retail, finance, and others. In healthcare, ML can be used to detect diseases, such as cancer, identify potential health risks while in cybersecurity it can be utilized to detect cyber-attacks and stop them before they become a major incident. In retail, ML can power recommendation engines on websites and apps, as well as help improve customer experience and purchase decisions.

ML is an ever-evolving technology, with new advancements and applications being adopted every day. It’s an increasingly essential technology for companies and organizations that need to make sense of large amounts of data. It also has potential to revolutionize many industries in the coming years.