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It must be programmed so that the different objects can be recognized according to their unique characteristics. Out machine learning model must be able to classify the different bunch of images. This is the most fundamental type of Supervised learning, which is called Classification. Once we complete the training, we should present an object's image that is not part of the training data, and the machine should be able to identify it by classifying all its previous learnings. When we train a machine learning model for image recognition, we present many images where every single image is attached to a label so that the data can be clearly labeled and gets stored in a machine learning model.
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Supervised learning works in a way that the computer can learn through the previous exposures for example, if a computer sees a car object and recognizes it like a car, then next time, it should be able to identify any different image of car object by identifying a lot of features that are similar to previously identified images of Car.
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However, computers are taught so that every time the particular blend of pixels comes in front of the computer it can recognize the type of image loaded into the model dataset. We feed a different set of some specific images into a machine where each image has a specific identifier to identify the type of the image. Being humans, we also learn by this model as we are taught to recognize different objects like a car by repeat exposures. One real-time example of supervised learning is recognizing different types of images using computers. It's like a hand-holding way of teaching the computers what to do. Supervised learning is similar to having a trainer or teacher who supervises all the machines' reactions and tells step-by-step solutions to specific problems. Machine Learning is broadly classified into two main categories.
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The code lets the machine learn and optimizes itself over repeated games. The chess game is a famous example where machine learning is being used to play chess. We need to provide training to the computers to find real-time solutions for the specific problems. Using machine learning, we don't need to provide explicit instructions to Computers for reacting to some special situations. Machine learning is the field of study that allows computers to learn without being explicitly programmed. In this tutorial, we will discuss what machine learning is, different types of it, including some real-life examples of machine learning. It is Apple's framework to use pre-trained models in iOS applications. In iOS applications, we use CoreML to incorporate machine learning in iOS applications. Next → ← prev Introduction to machine learning