Machine learning and deep learning are two branches of artificial intelligence that deal with teaching computers to learn from data, but they are different in several ways.

Machine learning is a broader term that refers to any type of algorithm or model that is able to improve its performance on a specific task over time through exposure to data. There are many types of machine learning algorithms, including decision trees, random forests, support vector machines (SVMs), and linear regression. Machine learning algorithms can be supervised, unsupervised, semi-supervised, or reinforcement learning.

Deep learning, on the other hand, is a specific type of machine learning that uses artificial neural networks with multiple hidden layers to model complex patterns in data. Deep learning is inspired by the structure and function of the human brain, and it has been particularly successful in tasks such as image and speech recognition, natural language processing, and game playing.

In summary, deep learning is a subset of machine learning and is focused on using neural networks to learn from large amounts of complex data. Machine learning, on the other hand, is a broader field that includes deep learning and other algorithms and techniques for learning from data.