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Master Complete Statistics For Computer Science – I

Course In Chance & Statistics Essential For Machine Studying, Synthetic Intelligence, Knowledge Science, Neural Community

What you’ll be taught

  • Random Variables
  • Discrete Random Variables and its Chance Mass Perform
  • Steady Random Variables and its Chance Density Perform
  • Cumulative Distribution Perform and its properties and software
  • Particular Distribution
  • Two – Dimensional Random Variables
  • Marginal Chance Distribution
  • Conditional Chance Distribution
  • Impartial Random Variables
  • Perform of One Random Variable
  • One Perform of Two Random Variables
  • Two Features of Two Random Variables
  • Statistical Averages
  • Measures of Central Tendency (Imply, Median, Mode, Geometric Imply and Harmonic Imply)
  • Mathematical Expectations and Moments
  • Measures of Dispersion (Quartile Deviation, Imply Deviation, Customary Deviation and Variance)
  • Skewness and Kurtosis
  • Anticipated Values of Two-Dimensional Random Variables
  • Linear Correlation
  • Correlation Coefficient and its properties
  • Rank Correlation Coefficient
  • Linear Regression
  • Equations of the Strains of Regression
  • Customary Error of Estimate of Y on X and of X on Y
  • Attribute Perform and Second Producing Perform
  • Bounds on Chances

Requirements

  • Information of Utilized Chance
  • Information of Calculus

Who this course is for:

  • Present Chance and Statistics college students
  • College students of Machine Studying, Synthetic Intelligence, Knowledge Science, Computer Science, Electrical Engineering , as Statistics is the prerequisite course to Machine Studying, Knowledge Science, Computer Science and Electrical Engineering
  • Anybody who desires to review Statistics for enjoyable after being away from college for some time.


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