The application of machine learning

saifulislam17@g

The algorithms generated are capable of generalizing behaviors from the data provided in the form of examples. Machine learning emerged in the mid-s with the application of neural networks and decision trees. It began to be used in complex prediction problems where classical statistical models were not efficient, such as, for example, speech and image recognition, prediction of non-linear time series, prediction of financial markets, recognition of written text, etc VIDEO How to eliminate barriers in Machine Learning and Deep Learning projects Discharge.


The main characteristic of this type of algorithms is that they are ID Number List capable of automatically readjusting themselves to improve their performance based on the number of successes and failures produced in a training process prior to their application and, during its real-time execution. . For example, an airport security control system could be programmed to perform facial recognition of all its users and check if they are on the list of most wanted criminals. Well, the search algorithm itself could learn by itself in two ways By providing images of random people and telling the algorithm whether or not they are criminals during the training process.





Rectifying your prediction in each observation in real time.  algorithms is very diverse. Other examples in the use of Machine Learning algorithms are a system that allows us to differentiate whether an email received in our inbox is spam or not based on the emails previously received, or the diagnosis of a patient based on their features and history. Machine learning models can be divided into two broad categories supervised and unsupervised learning . The key to including algorithms in one type or another will be in the type of data we use for training.