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 Two-Dimensional 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