Machine learning is a branch of artificial intelligence that deals with the design and development of algorithms that can learn from and make predictions on data. Machine learning algorithms have been used in a variety of applications, including facial recognition, stock market analysis, medical diagnosis, and fraud detection.
The term “machine learning” was coined in 1959 by Arthur Samuel, an American computer scientist who pioneered the field of artificial intelligence. Samuel defined machine learning as “the ability to learn without being explicitly programmed.”
Machine learning algorithms are typically classified into three types: supervised learning, unsupervised learning, and reinforcement learning. Supervised learning algorithms are trained using labeled data, such as a dataset where each example is labeled with the correct output value. Unsupervised learning algorithms are trained using unlabeled data; they try to find structure in the data by groups or clusters. Reinforcement learning algorithms are trained using a feedback signal (reward or punishment) in order to maximize some notion of cumulative reward.
The most common type of machine learning algorithm is the neural network which is inspired by the brain’s structure and function. Neural networks consist of layers of interconnected nodes (neurons) where each node performs a simple computation on its input values and passes the result to the next node in the layer. The final output value from the neural network is produced by applying a nonlinear function to the output values from the last layer of nodes.