Machine learning is a subfield of artificial intelligence (AI) and refers to the development of algorithms and models that enable computers to learn from data and recognise patterns without being explicitly programmed to do so. In other words, machine learning allows computers to learn from experience and improve their performance in performing certain tasks over time.

The basic idea of machine learning is to develop algorithms that are able to recognise complex patterns or correlations in large data sets and make predictions or decisions based on them. Machine learning can be divided into different categories, such as

1. Supervised learning: In this method, the algorithm is provided with training data with known input/output pairs. The algorithm learns to model the relationship between input and output data in order to make predictions with new, unknown input data.
2. Unsupervised learning: Here, the algorithm is provided with data without a known output. The algorithm attempts to recognise patterns, groupings or structures within the data in order to gain insights or organise the data.
3. Reinforcement learning: In this approach, the algorithm learns to make decisions by performing actions and receiving rewards or penalties based on them. The algorithm optimises its behaviour to maximise the cumulative reward over time.

Machine learning is used in many applications and industries, such as recognising spam emails, recommending products to customers, speech recognition, facial recognition, predicting share prices and many other areas. With the ability to learn from data and recognise patterns, machine learning enables the development of intelligent systems that have human-like capabilities and can perform numerous tasks more efficiently and accurately.