Introduction to Machine Learning

Introduction to Machine Learning 

As we humans always have the curiosity to do invention and do automation in the industrial area so that the work could be done automatically which requires lesser manpower.

Recently ISRO(Indian Space Research Organization) had carried there Chandrayan-2  Mission where a rover was attached with the orbiter and its aim to extract information from the moon after its soft landing. But the mission seems not to be successful as the lander had a hard landing and due to failure in its component and the connection with the lander (VIKRAM) is lost. ISRO scientist is analyzing the data to establish a connection with the lander Vikram. Although it's a very tedious task ISRO scientist are trying there best to make the mission 100% Successful. 

ISRO has many other Mission in hand to complete in the upcoming years. But how the scientist comes to any conclusion just like Chandrayan -1 mission which was not fully successful its seems ISRO did not achieved there target this time too although the landing site of the rover is different. ISRO scientist is planning Chandrayan-3 Mission with the same motive of extracting the information moon about its land and possibility of water.

No, Doubt Scientist will analyze the previous mission that why it was not fully successful and will gain some intuition from them and apply the solution to the Chandrayan-3 mission to make it fully successful. Here the main source of learning for the scientist is there domain knowledge about the space exploration and Data which they gather from the Space.

So, One thing is clear when we do any mistake we learn a lesson from it and make sure to not to repeat.  Here may be our brain is storing information and with signal processing in our nerves, we are able to take the decision.

How does the machine's take a decision?


Here machine learning techniques come to play. According to Arthur Samuel
" Machine learning is the field of study that gives the computer the ability to learn without being explicitly programmed"

In Machine learning to perform any task, we design a machine learning model which performs according to the desired output 

This model can be assumed as a time machine model where we give certain input and based on analyzing the input we get a certain output.In mathematics, we call it a function.

Machine learning can also be defined as 
"A computer program which learns from experience E with respect to some task T and some performance measure  P, if its performance on T as measured by P, improved with experience E"

For example Suppose your email program watches which emails you do or do not mark as spam, and based on that learns how to filter spam then its task is to classify emails as spam or not spam.

There are various machine learning algorithm such as 

1) Supervised Learning
2) Unsupervised Learning
3) Reinforcement Learning
4) Recommender System

 

Supervised Learning: In supervised we teach the computer how to do somethong, then let it use it's new found knowledge to do it.Here we will have labels(output) along with input features

Unsupervised Learning: In unsupervised learning we let the computer learn how to do something, and use this to determine structure and pattern in data. Here we won't have output labels

Reinforcement Learning:It is about taking suitable action to maximize reward in a particular situation. It is employed by various software and machines to find the best possible behavior or path it should take in a specific situation.

We will learn more about machine learning in later sections






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