Master Complete Statistics For Computer Science - I

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Master Complete Statistics For Computer Science - I
Video: .mp4 (1280x720, 30 fps(r)) | Audio: aac, 44100 Hz, 2ch | Size: 6.46 GB
Genre: eLearning Video | Duration: 156 lectures (21 hour, 20 mins) | Language: English
 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

Description

In today's engineering 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 engineering 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 engineering 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:

    Introduction

    Discrete Random Variables

    Continuous Random Variables

    Cumulative Distribution Function

    Special Distribution

    Two - Dimensional Random Variables

    Random Vectors

    Function of One Random Variable

    One Function of Two Random Variables

    Two Functions of Two Random Variables

    Measures of Central Tendency

    Mathematical Expectations and Moments

    Measures of Dispersion

    Skewness and Kurtosis

    Statistical Averages - Solved Examples

    Expected Values of a Two-Dimensional Random Variables

    Linear Correlation

    Correlation Coefficient

    Properties of Correlation Coefficient

    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

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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