INTRODUCTION TO PROBABILITY:

Probability is a mathematical framework that performs actions contain some uncertainty.consider the real life scenario to understand the concept of probability.when the person playing the football.he may or maynot get the goal.this event deals with uncertainty right.uncertainty nothing but we cannot sure about the event that is going to definitely take place.consider the another example when we toss the coin there would be probability 50% head occur or50% tail occur.in this event also we are not sure about the event.in all these situation probability exist.its possible to convert uncertain actions to some what certain action by applying the probability concept.generally probability range lies between 0and1.means if it's zero then there would be no chance the event happen.if its 1 then the event is definetly going to happen.or even the probality is like 0.2 which is 20%chance the event would be take place.it build the base for machine learning.probability enables the machine learning to predict the future .

LAW OF LARGE NUMBERS:

LETS brief this .it tells that as we conduct the more number of experiment the more will probability of event tend to the expected probability

Means consider tossing coin event according our assumption when we toss the coin twice we expect that one time head will occur another time tail would be happen because probablity is 0.5 .so this will may or maynot going to happen .if we conduct this event for 1000 times then probablity tend to be equal to expected probablity

PROBABILITY DISTRIBUTION:

Now we are going to understand about the probablity distribution and its different types it means its an a systematic way for assigning probablity for the each event .as we know there are 2 kind PMF(probablity mass function) its associated with the discrete random variable .again what is discrete random variable it mean a random variable which can take only finite number of values .any probability function which assign the probablity for each event.

PDF(probablity distribution function)

It generally deals with the continuous random variable.a variable which take infinite or many number of possible values.the real world examples of the continous random variable is :consider X is random variable associated with amount of rainfall in a given day where. X[0,infinite] where the pdf which fall in that range

MEASURE OF CENTRAL TENDENCY:

measure of central tendency is a single value that is going to describe the entire data set

There are three kinds of measure of central tendency

1)mean

2)median

3)mode

Mean:its average of the data set.it means summing all the data set which is divided by total number of data set

But it has disadvantage if our data set which contain outliers then the mean get influenced by that outliers and make the wrong prediction.so we can analyse the data using the median the median is not influenced by the outliers

MEDIAN:

Median os the middle most score in the given data set arranged in either ascending or descending order.

In any situation. The median is not influenced by the outliers.so considering the median for data analysis is best.

MODE:

Mode is the most frequent score in the data set

Means the score which is occur more number of times

There is no condition that mode can occur only one time even two modes can exist called bi modal or many number of modes also exist in the given data set called multi modal

But the mean and mode exist only once in the data set

Variance:

Varience means its a measure of how much data point is differ from mean

Or how for the data point which is spread out from the mean or average value.

If the variance is more that means more data is scattered or spread out from the mean

Overall the concept of probablity enables the machine learning to predict the future based on the historical data