Machine Learning: What it is and Why it Matters

Today you may have heard the word machine learning,Every time when you search on search engine like google or bing you may notice that it index pages as you want this become possible due to machine learning.Facebook or Apple photos app may recognize you and your friends from the pic this become possible due to machine learning.Your email system automatically detect a spam mail and filter it due to machine learning.

Today machine learning give new wings to many area like:-
1.)Medical and Healthcare

2.)Researches and Inventions

3.)Real Estates

4.)Cosmos Researcher

5.)Natural Language processing

6.)Computer vision

7.)Data Mining

and counting.
So now the question rises what machine learning is?

Two definitions of Machine Learning are offered:-
Arthur Samuel described it as: "the field of study that gives computers the ability to learn without being explicitly programmed." This is an older, informal definition.
Tom Mitchell provides a more modern definition: "A computer program is said to learn from experience E with respect to some class of tasks T and performance measure P, if its performance at tasks in T, as measured by P, improves with experience E."
In general, any machine learning problem can be assigned to one of two broad classifications:

1.)Supervised learning.

2.)Unsupervised learning.

Supervised learning:-
In supervised learning, we are given a data set and already know what our correct output should look like, having the idea that there is a relationship between the input and the output.
Supervised learning problems are categorized into "regression" and "classification" problems. In a regression problem, we are trying to predict results within a continuous output, meaning that we are trying to map input variables to some continuous function. In a classification problem, we are instead trying to predict results in a discrete output. In other words, we are trying to map input variables into discrete categories.


Unsupervised learning:-

Unsupervised learning allows us to approach problems with little or no idea what our results should look like. We can derive structure from data where we don't necessarily know the effect of the variables.
We can derive this structure by clustering the data based on relationships among the variables in the data.
With unsupervised learning there is no feedback based on the prediction results.
Steps to apply Machine Learning:-
1.)Gathering data from various sources:- We have to use large dataset to generate a result or in other words just to train our systems.
2.)Cleaning data to have homogeneity:-We have to clean our dataset to filter out the important imformation from it .
3.)Model Building:-Model Building is the important aspect of machine learning ,in model building we select the right ml algorithm to apply on it.
4.)Gaining Insights:-By applying model building we now have the result but now we have to build a observing point for the result.
5.)Data Visualization:-After getting the result we have to plot a graph for the data analysis and more.

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