Master Complete Statistics For Computer Science – I
Course In Probability & Statistics Important For Machine Learning, Artificial Intelligence, Data Science, Neural Network
What you’ll learn

Random Variables

Discrete Random Variables and its Probability Mass Function

Continuous Random Variables and its Probability Density Function

Cumulative Distribution Function and its properties and application

Special Distribution

Two – Dimensional Random Variables

Marginal Probability Distribution

Conditional Probability Distribution

Independent Random Variables

Function of One Random Variable

One Function of Two Random Variables

Two Functions of Two Random Variables

Statistical Averages

Measures of Central Tendency (Mean, Median, Mode, Geometric Mean and Harmonic Mean)

Mathematical Expectations and Moments

Measures of Dispersion (Quartile Deviation, Mean Deviation, Standard Deviation and Variance)

Skewness and Kurtosis

Expected Values of TwoDimensional Random Variables

Linear Correlation

Correlation Coefficient and its properties

Rank Correlation Coefficient

Linear Regression

Equations of the Lines of Regression

Standard Error of Estimate of Y on X and of X on Y

Characteristic Function and Moment Generating Function

Bounds on Probabilities
Requirements

Knowledge of Applied Probability

Knowledge of Calculus
Who this course is for:
 Current Probability and Statistics students
 Students of Machine Learning, Artificial Intelligence, Data Science, Computer Science, Electrical Engineering , as Statistics is the prerequisite course to Machine Learning, Data Science, Computer Science and Electrical Engineering
 Anyone who wants to study Statistics for fun after being away from school for a while.