Why is machine learning so important

Why is machine learning so important?

machine learning 

Machine Learning (Introduction + Data Mining VS ML)

We will learn about machine learning and data extraction, so we'll start by identifying the difference between data mining and machine learning now, and mining includes briefly finding patterns in data and this can be done automatically or semi-automatically, what I mean by finding patterns is that I had a huge database containing On many different tables, it essentially extracts all the knowledge that I can request from this data to develop a consistent set of outputs or reports based on this data, and you clearly need knowledge about the structure of the data set to be able to extract this knowledge to So I would like to summarize these for us

What knowledge can I gain from the data that I have on the other hand we have a machine that knows my own state that this is data mining, so machine learning basically includes algorithms that automatically improve through data-based experience so that both processes are completely related and I ask clearly The data will be able to learn, so I learn based on this data, and it usually includes two sets of data that resemble training data and test data, so how do we really determine that the IP address that mutrah knows well, it's very difficult to actually determine what we should start with Find out what Is knowledge,

So there is an example I would like to quote that says when the vine grows around a stick or something that you do not say has already learned how to grow around it, as you say. He was trained himself to do so, learning without purpose is just training or otherwise, that implies that the purpose of this comes from a book called data mining from en H Witten and E Frank which is what we will use to attribute most of these videos too so the learning on any If the case involves the purpose, so I summarize it for us based on this data that I have. How can I learn to predict and classify new data, so in short, we have all these data available to us? What can I conclude? This and how can I classify a new data set similar to a let's say group or have properties in the effect or impact vectors where we will be communicating with them, so there are different types of machine learning, the two most common which you will see around them, are supervised and not subject to control

Supervised and non-supervised learning 

Now, what is the difference between these two types is good in that it is quite complicated to actually determine that, but Wikipedia contains a very lengthy article on both topics you may want to check but in short we can say that in supervised learning we deduce a function from supervised training data It is clear that this data includes the input and output and in the known supervised learning, we do not have this information, so we will actually try to find the structure of the data, so let's assume that there is an example of supervised learning? And the actual trains that have different schedules and how often the trains depend on data such as flight, weather conditions, etc., so we clearly have an actual schedule with historical data that every doctor takes into account the date and time if the train is on time or not, and the actual special weather Based on this information

 We can deduce our common characteristics and even predict in the future if the specific weather will affect the extent of the delay or delay the train is all this information, then all of this data we have collected is actually useful to be able to develop new answers based on this data, so it is our really interesting topic, but there are also many tasks involved in machine learning, the most common ones being group classification and regression and two common types are linear and logistic regression