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Artificial intelligence can make sense of highly voluminous and complex data, understand the intuition behind repetitive networks, enable machines to learn from experience and adapt to new inputs.
Artificial intelligence algorithms integrate almost human-able operations into the area they are used in, and they can easily analyze, predict and process the area they are used in.
Artificial intelligence algorithms have a structure that constantly learns. Based on the learning mechanism of the human brain, it aims to give the necessary reactions where necessary and to give the desired requests from you easily by learning the movements, speeches and attitudes that a baby learns from the beginning to the end.
Artificial intelligence algorithms analyze all the meaningless and meaningless data you have, arrange them as they should be and show you the clear results. Too much data does not change this order.
Deep learning achieves incredible accuracy on the things we use, which is impossible through deep neural networks. As we use it, the accuracy and learning rate increases day by day.
While algorithms learn by themselves, the data itself can become an idea. All the desired answers are hidden in the data. Data plays an important role here. In order to make sense of complex data that traditional models cannot solve and make it efficient, it is necessary to apply artificial intelligence.
Machine learning is to create algorithms that can learn structurally and functionally about a particular data set and make predictions, inspired by human learning processes. Basically, machine learning applications proceed from the logic of self-learning the concepts that a person sees and hears. It establishes a connection between the given input and the desired output by adopting the data sets presented to it.
Machine learning consists of algorithms that can become more accurate in predicting results instead of working with the conditions set by the programmer, such as software. Its basic basis is to make probabilistic estimates by applying the model created with input and output data to data that it has not encountered before in the same situation.
The algorithms to be used vary with the separation of data into numerical and categorical terms. Numerical data are quantitative and help to generate predictable meaningful outcomes, while categorical data are expressed by classification.
For example look at this dataset:
Gender categorical data, height, weight and age are numerical data. We use height, weight and age when classifying the sexes. If we want to estimate weight; We use gender, height and age. Thanks to machine learning, we can classify the genders of people whose age and height we know and estimate their weight.
We can easily weight/age ranges by looking at the chart. We can simply observed that women are weaker than men, in the same way that men become heavier as they get older.
We can analysis machine learning in 3 different type algorithms
We know what the dataset is and what the output we want from this data should be. Supervised provides a match between input data and output data by giving the data and the results from the data back to the machine from the beginning. Thus, the machine learns the relationship between data.
We can create various models and structures by clustering the data based on the relationship between variables without specifying any grouping. We use it when we have no idea what the output we want from the data looks like. We can create a model from data that we do not know the effect of variables.
Reinforcement learning shows how we can learn to make the right decisions if a system that perceives its environment and can make decisions on its own can achieve its goal.