Types of Machine learning

Supervised

Supervised learning is if we are see here mathematically, where we have input variables X and output variable is Y and we using an algorithm to learn the mapping function from the input to the output. That is y Affects the goal is to approximate the mapping function. So well that whenever we have a new input data X could predict the output variable. That’s why for that data right ,let simplify the definition of supervised learning so we can rephrase the understanding of the mathematical definition as machine learning method where each instances of a training data set is composed of different input attribute and an expected output the input attributes of training data set can be of any end of data it can be a pixel of image. It can be a value of data base row or it can be audio frequency histogram right for each input instance and expect output value Associated value can be discrete representing a category or can real or continuous value in either case. The algorithm learns the input pattern that generate the expected output row once the algorithm strain, it can be used to predict the correct output of never seen input.

Supervise Learning Algorithms

1 linear regression random forest.

2 Support vector machines.               

Now let see some of the popular uses cases of supervise learning so we have Google call or Donna Codon or any other speech Automation in mobile phone trains using your voice and one strain it start working based on the training. This is application of supervised learning. Whether up based on some o f the prior knowledge like when sunny temperature is high. Fire when cloudy when it is cloudy humidity is higher any kind of that they predict the parameters for a given time so this also example of supervise learning as we are feeding data to the machine ans telling that whenever it is sunny the temperature is should be higher whenever it is cloudy. The humidity should be higher. So it’s an example supervised learning.