Master Complete Statistics For Computer Science – I

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 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

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.

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