Machine learning is a branch of artificial intelligence (AI) that involves teaching computers to learn from data, without being explicitly programmed to perform specific tasks. In other words, it's a way of training machines to learn and improve on their own through experience, rather than relying on explicit programming. Machine learning algorithms use statistical techniques to identify patterns and relationships in data, and then use those patterns to make predictions or take actions. These algorithms can be used for a variety of tasks, such as image recognition, speech recognition, natural language processing, recommendation systems, and predictive analytics.

Machine Learning offers many benefits, including improved accuracy and speed in decision-making, enhanced efficiency and productivity, and the ability to discover hidden patterns and insights in data. It can also automate repetitive tasks, reduce human error, and optimize business processes.

The three main types of Machine Learning are supervised, unsupervised, and Reinforcement Learning. Supervised Learning involves learning from labeled data, while Unsupervised Learning involves learning from unlabeled data. Reinforcement Learning consists in learning through a system of rewards and punishments.

Machine Learning is used in business to automate tasks, optimize processes, reduce costs, and improve customer experience. It is used for fraud detection, predictive maintenance, recommendation systems, customer segmentation, and image recognition.

Deep Learning is a subset of Machine Learning that uses artificial neural networks to learn and make decisions. It is beneficial for tasks that involve large amounts of data, such as image or speech recognition, natural language processing, and autonomous driving.

Computer Vision is a field of study that focuses on enabling machines to interpret and understand visual data from the world around them. It involves building algorithms and models that allow devices to recognize and classify objects, people, and scenes in images and videos.