Machine learning (ML) is a growing field of computer science, where computers are taught to perform tasks based on data that has been pre-labeled with predetermined instructions. In the early 2000s, the concept of artificial intelligence was far from mainstream consciousness. Many saw it as a dark future where robots would take over the world by defeating humans in athletic competitions. For the most part, this fear was unwarranted as computer systems continue to improve. It’s far more likely that machines will replace humans in jobs that are repetitive and boring, such as stocking shelves or driving a truck.

So, is machine learning the next big thing? The term was first coined by technology expert Nathan G. Wolfe in his book “The End of Big Data,” where he compares machine learning to big data. He claims that both could eventually replace the role of human intellectuals, but only if they’re used for the right purpose. According to Wolfe, big data is the stuff that has been generated by a computer for a very specific purpose. Machine learning, on the other hand, machine learning Onis the action of a computer figuring out how data was generated and then using that information to make decisions.

Machine learning is the study of how computers can learn to perform tasks without being explicitly programmed. The latest craze in the field is deep learning, which allows computers to learn without human supervision by analyzing huge data sets to find patterns that eventually lead to a solution. The practical applications of learning machines are wide-ranging, from autonomous cars to artificial intelligence that enables machines to recognize everyday objects, such as a photo of a cat or a photo of a person. Since machine learning is still in its early stages, it is wise to be cautious when using it—especially since there is no guarantee that machines will always be “right.”