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[Udemy] Master Complete Statistics For Computer Science – I

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What you’ll learn

  • Random Variables
  • Discrete Random Variables and its Likelihood Mass Perform
  • Steady Random Variables and its Likelihood Density Perform
  • Cumulative Distribution Perform and its properties and utility
  • Particular Distribution
  • Two – Dimensional Random Variables
  • Marginal Likelihood Distribution
  • Conditional Likelihood Distribution
  • Unbiased Random Variables
  • Perform of One Random Variable


  • Data of Utilized Likelihood
  • Data of Calculus


In at present’s engineering curriculum, matters on likelihood and statistics play a significant function, because the statistical strategies are very useful in analyzing the information and decoding the outcomes.

When an aspiring engineering pupil takes up a venture or analysis work, statistical strategies turn into very useful.

Therefore, the usage of a well-structured course on likelihood and statistics within the curriculum will assist college students perceive the idea in depth, along with making ready for examinations reminiscent of for normal programs or entry-level exams for postgraduate programs.

In an effort to cater the wants of the engineering college students, content material of this course, are properly designed. On this course, all of the sections are properly organized and introduced in an order because the contents progress from fundamentals to larger stage of statistics.

In consequence, this course is, in reality, pupil pleasant, as I’ve tried to elucidate all of the ideas with appropriate examples earlier than fixing issues.

This 150+ lecture course consists of video explanations of all the pieces from Random Variables, Likelihood Distribution, Statistical Averages, Correlation, Regression, Attribute Perform, Second Producing Perform and Bounds on Likelihood, and it consists of greater than 90+ examples (with detailed options) that will help you check your understanding alongside the best way. “Grasp Full Statistics For Laptop Science – I” is organized into the next sections:

  • Introduction
  • Discrete Random Variables
  • Steady Random Variables
  • Cumulative Distribution Perform
  • Particular Distribution
  • Two – Dimensional Random Variables
  • Random Vectors
  • Perform of One Random Variable
  • One Perform of Two Random Variables
  • Two Capabilities of Two Random Variables
  • Measures of Central Tendency
  • Mathematical Expectations and Moments
  • Measures of Dispersion
  • Skewness and Kurtosis
  • Statistical Averages – Solved Examples
  • Anticipated Values of a Two-Dimensional Random Variables
  • Linear Correlation
  • Correlation Coefficient
  • Properties of Correlation Coefficient
  • Rank Correlation Coefficient
  • Linear Regression
  • Equations of the Strains of Regression
  • Normal Error of Estimate of Y on X and of X on Y
  • Attribute Perform and Second Producing Perform
  • Bounds on Possibilities

Who this course is for:

  • Present Likelihood and Statistics college students
  • College students of Machine Studying, Synthetic Intelligence, Information Science, Laptop Science, Electrical Engineering , as Statistics is the prerequisite course to Machine Studying, Information Science, Laptop Science and Electrical Engineering

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