In Machine learning each feature or columns is called as dimension. Reducing the number of column or feature is called as a dimension reduction. Dimension reduction is important because it helps our model to predict more accurate output as it gets less confused.
Scaling down the feature is very much important because if we don’t scale down the values the value of the Euclidian distance Gradient descent will also increases.
Hypothesis testing is done to evaluate two mutually exclusive statements which means either one of the statements can be true on a population data using a sample data.
The binomial distribution model allows us to compute the probability of observing a specified number of "successes" when the process is repeated a specific number of times (e.g. in a set of students grade) and the outcome for a given student's grade is either a success or a failure.
Covariance is used for describing the variance between two Random Variables.
Let’s understand it by an example.
Sales of ice-cream in a shop with respect to increase in temperature.
In Mathematics and Statistics, we deal with data set if the data set is small there are no issues but when it comes to large amount of data it becomes a bit difficult to represent it so to know the big picture of such large amount of data Mean, Median, Mode comes into rescue.It gives us the insights of the data set.
We will understand Mean, Median, Mode considering data set of the salary package of students offered by the companies.
The very first step of statistical analysis is to find out the data is a population or sample. So, what the hell is population and sample and how they are different from each other…?
Let’s understand it by an example
Suppose you want to survey the salary of the people living in the particular state.
