Master Complete Statistics For Computer Science

Last updated 11/2022MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHzLanguage: English | Size: 6.47 GB | Duration: 21h 21m

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

Maal 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

Description

In today’s eeering curriculum, topics on probability and statistics play a major role, as the statistical methods are very helpful in analyzing the data and interpreting the results.When an aspiring eeering student takes up a project or research work, statistical methods become very handy.Hence, the use of a well-structured course on probability and statistics in the curriculum will help students understand the concept in depth, in addition to preparing for examinations such as for regular courses or entry-level exams for postgraduate courses.In order to cater the needs of the eeering students, content of this course, are well designed. In this course, all the sections are well organized and presented in an order as the contents progress from basics to higher level of statistics.As a result, this course is, in fact, student friendly, as I have tried to explain all the concepts with suitable examples before solving problems.This 150+ lecture course includes video explanations of everything from Random Variables, Probability Distribution, Statistical Averages, Correlation, Regression, Characteristic Function, Moment Generating Function and Bounds on Probability, and it includes more than 90+ examples (with detailed solutions) to help you test your understanding along the way. "Master Complete Statistics For Computer Science - I" is organized into the following sections:IntroductionDiscrete Random VariablesContinuous Random VariablesCumulative Distribution FunctionSpecial DistributionTwo - Dimensional Random VariablesRandom VectorsFunction of One Random VariableOne Function of Two Random VariablesTwo Functions of Two Random Variables Measures of Central TendencyMathematical Expectations and MomentsMeasures of DispersionSkewness and KurtosisStatistical Averages - Solved ExamplesExpected Values of a Two-Dimensional Random VariablesLinear CorrelationCorrelation CoefficientProperties of Correlation CoefficientRank Correlation CoefficientLinear RegressionEquations of the Lines of RegressionStandard Error of Estimate of Y on X and of X on YCharacteristic Function and Moment Generating FunctionBounds on Probabilities

Who this course is for

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

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