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Machine learning is a branch of artificial intelligence that focuses on the development of algorithms that allow computers to learn from and make predictions or decisions based on data. Instead of being explicitly programmed to perform a task, machines use these algorithms and large amounts of data to identify patterns and improve their performance on their own.

There are several approaches and techniques in machine learning, including:

  1. Supervised Learning: In this approach, the machine is provided with a training data set with the correct answers (labels). The goal is for the machine to learn a general function that maps inputs to the correct outputs.
  2. Unsupervised Learning: Here, the data is not labeled and the goal is for the machine to identify structures or patterns on its own. A common example is clustering.
  3. Reinforcement learning: In this approach, the model learns from feedback of its actions in an environment. You receive rewards or penalties based on the quality of your actions, and over time you seek to maximize your total reward.
  4. Semi-supervised learning: It is a combination of supervised and unsupervised learning, where a small set of labeled data is used along with a larger set of unlabeled data to improve the performance of the model.
  5. Deep Learning: Refers to neural networks with many layers. These networks are capable of learning complex features from data and have been instrumental in recent advances in areas such as computer vision and natural language processing.

Machine learning has applications in a wide variety of fields, from recommending products in online stores to detecting diseases in medical images. The increasing availability of data and advancement in computing capacity have driven the growth and relevance of this area in the last decade.