Machine learning is a branch of artificial intelligence (AI) and computer science that focuses on the use of data and algorithms to imitate the way humans learn, gradually improving its accuracy. It is an umbrella term for solving problems for which development of algorithms by human programmers would be cost-prohibitive, and instead the problems are solved by helping machines "discover" their "own" algorithms, without human intervention. Machine learning algorithms use computational methods to "learn" information directly from data without relying on a predetermined equation as a model.

Key features of machine learning include:

• Supervised Learning: This technique trains a model on known input and output data so that it can predict future outputs.

• Unsupervised Learning: This technique finds hidden patterns or intrinsic structures in input data.

• Deep Learning: This is a specialized form of machine learning that uses neural networks with many layers to learn and improve performance.

Machine learning is widely applicable across many industries. It is used in recommendation engines, self-driving cars, healthcare, and more. Machine learning algorithms find natural patterns in data that generate insight and help make better decisions and predictions.

In summary, machine learning is a powerful tool for solving complex problems and automating tasks, requiring deep expertise and significant resources. It is a subfield of artificial intelligence that uses data and algorithms to imitate the way humans learn, gradually improving its accuracy.