Artificial Intelligence refers to the simulation of human intelligence in machines that are programmed to think and learn. These systems are capable of performing tasks that typically require human intelligence, such as visual perception, speech recognition, decision-making, and language translation.
Machine Learning is a subset of artificial intelligence that involves the use of algorithms and statistical models to enable computers to improve their performance on a specific task through experience. ML algorithms build a model based on sample data, known as "training data," to make predictions or decisions without being explicitly programmed to perform the task.
Neural Networks are a series of algorithms designed to recognize patterns, inspired by the way the human brain operates. They interpret sensory data through a kind of machine perception, labeling, or clustering of raw input. These networks consist of layers of nodes, or neurons, with each layer transforming the input data into a more abstract representation.
Natural Language Processing is a field of artificial intelligence that focuses on the interaction between computers and humans through natural language. The ultimate goal of NLP is to enable computers to understand, interpret, and respond to human language in a way that is both valuable and meaningful.
Big Data refers to the large volume of data that inundates businesses on a day-to-day basis. But it's not the amount of data that's important. It's what organizations do with the data that matters. Big Data can be analyzed for insights that lead to better decisions and strategic business moves.
An Algorithm is a set of rules or instructions given to an AI, ML, or computer system to help it learn on its own. Algorithms are the building blocks of computer programs, and they are used to perform calculations, data processing, and automated reasoning tasks.