Machine learning (ML) is a type of artificial intelligence (AI) that enables computers to learn and make predictions or decisions based on data, without being explicitly programmed to do so. Basically, it's a way to teach machines to recognize patterns and make decisions on their own.

To give you an example, let's say you want to teach a computer how to recognize images of cats. You would start by feeding the computer a large dataset of images, some of which have cats and some of which don't. The computer would use algorithms to analyze the data and learn how to distinguish between images with cats and images without cats. Once it's learned this, you can give it new images that it's never seen before, and it will be able to recognize whether they have a cat in them or not.

Machine learning is used in many different industries, such as healthcare, finance, and marketing. It's a powerful tool that can help us make better decisions, automate processes, and even create new products and services.
Machine learning is a complex and rapidly evolving field, and it would be difficult to cover all aspects of it in a single conversation. Machine learning involves teaching computers to learn and make predictions or decisions based on data, without being explicitly programmed to do so.

There are different types of machine learning, such as supervised learning, unsupervised learning, and reinforcement learning, each with its own set of techniques and applications. Some common examples of machine learning applications include image and speech recognition, natural language processing, recommendation systems, fraud detection, and predictive maintenance.
Machine learning requires a combination of skills from various fields such as mathematics, statistics, computer science, and domain expertise. There are also many programming languages and frameworks that are commonly used in machine learning, such as Python, TensorFlow, and scikit-learn.
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