Uses data and answers to create algorithms

[numberlist]

For example: In the cardiology example above, if we were to use a traditional algorithm, we would take data like heart rate and BMI and create an algorithm to determine whether or not the heart is failing. It would essentially be an “if-then-else” statement.


When we input data, we would get an answer based on the algorithm that was defined. In contrast, Machine Learning will use data and answers to create algorithms. The model will determine the logic and parameters in the algorithm itself.


Types of Machine Learning Supervised greece whatsapp number data Learning The algorithm is trained on data labeled by humans. The more samples provided, the more accurate the algorithm becomes in classifying new data. For example: Feed the Machine Learning program a large amount of bird images and train the model to return the label "bird" whenever it is given an image of a bird.


Unsupervised Learning The algorithm is fed unlabeled data and finds patterns on its own. Useful for data clustering, where data is grouped according to similarity to neighboring data points. For example: Feed the Machine Learning algorithm a continuous stream of network traffic and let it learn about normal baseline network activity, as well as unusual and potentially malicious activities occurring on the network.