MACHINE can Learn if you are a Good TEACHER

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MACHINE can Learn if you are a Good TEACHER

Winter training in Machine Learning

WHAT IS MACHINE LEARNING?

Machine Learning is an operation of Artificial Intelligence that provides systems the ability to cardinally burn the midnight oil and improve from experience without being explicitly programmed. The intent of machine learning generally is to apprehend the structure of data and fit that data into models that can be understood and exploit by people. The term machine learning and artificial intelligence are closely related and it’s not wrong to say that abstraction level between these are very thin fairly in line and they can be interchangeably used. Machine Learning is an algorithm allows us to built set learning machine that evolves by itself without being explicitly programmed.

 Machine learning is closely related to DATA MINING. Like if we work on an example then we can understand that how machine learning is closely related to data mining i.e SPAM EMAIL. In, the mail session we have found some of the mail lies in the inbox and some of them lie in spam. This is because of Machine Learning as there is huge junk of data and your program and algorithm are designed in such a  manner. So, that it can predict that whether this email is spam or a useful mail i.e needed to be delivered. Based on users behavior, data patterns and past experiences it makes the important future decision.

 

Why Machine Learning?

Suppose, if sometimes we need navigation on a map or deep ocean where no humans can actually go there, then we need a machine to automatically adapt towards that environment. This is one of the examples where machine learning is really important. Have you ever used applications like Flipkart, Amazon, Snapdeal they help buyers in recommending products which are not possible by manual calculators the recommendation is done on the basis of past and future searches specified upon the product one is looking for this also done by machine learning? Analysing huge sensor data and predicting the outcome e.g in forecasting systems is not possible by manual calculations then, the machine learning help us in this. These were the very common examples of machine learning as this proves how much important is machine learning.

 In machine learning, we use algorithms to let computer understand where the algorithm is a set of explicitly programmed instructions used by computers to calculate or solve the problem. Machine learning algorithm allows the computer to train on data inputs and to use statistical analysis in order to find out the output values within a specific range.

 

Steps to be followed for Machine Learning?

There are certain procedures that are needed to be followed in machine learning:

  • Collecting Data
  • Cleaning Data
  • Analysis of Data
  • Train the algorithm
  • Test the algorithm
  • Use it

Above, the mentioned point can work as a staircase to work on Machine Learning. As we have been across that the first step is to follow data mining and then, from that collection of data we have to get rid of the data i.e not useful for us. Further, we work on the resolution of the data that has been obtained from the collection of data we have. Thus, the journey of machine learning that is training and testing starts.

 

Machine learning works upon two different aspects, they are:

  1. Supervised Learning
  2. Unsupervised Learning.

So, if you are training your machine, learning task for every input with the corresponding target it is called Supervised Learning and after that, your machine will be able to provide a target or any new input after sufficient data.  SUPERVISED LEARNING is the machine learning task of speculating an operation from labeled training data. Under the table of supervised learning, we found two stand that is TRAINING and TESTING which works upon DATA.

Training works on the algorithm and trained your algorithm on the basis of that data and create a model. Now, you have test data as well with the help of this we determine the accuracy as we use that test we actually see what will be the outcome of that algorithm and when the outcome matches with test data then accuracy is high and thus, accuracy is tested.

 Whereas, UNSUPERVISED LEARNING then if we are training machine with a set of input not all input after this machine will be able to find structure and relationship between different input. In this, the algorithm must learn to reach a certain goal on unlabeled data.

 

From above, we get the glimpse of MACHINE LEARNING i.e why the enlightenment towards this technology is running so vigorously. The era is of upcoming more machines which will stand upon the technology of Machine Learning

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