Machine Learning refers to a group of computer algorithms that can learn from examples and improve themselves without being explicitly coded by a human. ML is an artificial intelligence subset in which data is coupled with statistical approaches to predict an output that may be used to generate actionable insights. The goal of machine learning is to enable computers to learn from experience and make intelligent decisions, just like humans do. Over time, as the system is exposed to more data, it can continue to learn and improve its predictions.
Say you want to create a machine learning model to predict house prices based on their size, location, and number of bedrooms. You start by collecting data on a large number of houses in the area, including those parameters. Then, you use an algorithm to analyze this data and find the relationships between these features and house prices. Once the algorithm has learned from the data, you can use it to make predictions on the price of new houses based on the same characteristics — size, location, and number of bedrooms.
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Model deployment is the stage where the ML model transitions from a theoretical construct into a practical component of business processes, applications, or systems.
A pre-trained model is a ready-made machine learning model that has been previously trained on a substantial dataset.