Machine Learning is a subset of artificial intelligence that involves the development of algorithms and models that enable computers to learn patterns and make predictions or decisions without explicit programming.

Machine Learning can be categorized into three main types: supervised learning, unsupervised learning, and reinforcement learning. Supervised learning involves labeled data, unsupervised learning deals with unlabeled data, and reinforcement learning focuses on decision-making through trial and error.

Machine Learning models learn from data by being trained on a dataset. During training, the model adjusts its parameters to minimize the difference between its predictions and the actual outcomes in the training data. This process is typically done through optimization algorithms.

Machine Learning is a broader field that includes various techniques, while Deep Learning is a subfield of Machine Learning that specifically deals with neural networks, particularly deep neural networks. Deep Learning is often used for tasks such as image recognition and natural language processing.

Machine Learning is applied in various industries, including healthcare (diagnosis and treatment prediction), finance (fraud detection and risk assessment), marketing (customer segmentation and personalized recommendations), and autonomous vehicles (object detection and decision-making).