Course In Probability & Statistics Important For Machine Learning, Artificial Intelligence, Data Science, Neural Network
☑ 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
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
English
Language
Introduction
Master Complete Statistics For Computer Science – I
Course Structure and Curriculum
Random Variables Definition
Discrete Random Variables
Discrete Random Variables – Concept
Discrete Random Variables – Solved Example 1 and 2
Discrete Random Variables – Solved Example 3
Discrete Random Variables – Solved Example 4
Discrete Random Variables – Solved Example 5
Continuous Random Variables
Continuous Random Variables – Concept
Continuous Random Variables – Solved Example 1 and 2
Continuous Random Variables – Solved Example 3
Continuous Random Variables – Solved Example 4
Continuous Random Variables – Solved Example 5
Continuous Random Variables – Solved Example 6
Continuous Random Variables – Solved Example 7
Continuous Random Variables – Solved Example 8
Cumulative Distribution Function
Cumulative Distribution Function – Concept
Cumulative Distribution Function – Solved Example 1
Cumulative Distribution Function – Solved Example 2
Cumulative Distribution Function – Solved Example 3
Cumulative Distribution Function – Solved Example 4
Cumulative Distribution Function – Solved Example 5
Cumulative Distribution Function – Solved Example 6
Special Distribution
Special Discrete Distribution
Special Continuous Distribution
Special Distribution – Solved Example 1
Special Distribution – Solved Example 2
Two – Dimensional Random Variables
Two – Dimensional Random Variables – Concept
Cumulative Distribution Function – Concept
Marginal Probability Distribution – Concept
Conditional Probability Distribution – Concept
Two – Dimensional Random Variables – Solved Example 1
Two – Dimensional Random Variables – Solved Example 2
Two – Dimensional Random Variables – Solved Example 3
Two – Dimensional Random Variables – Solved Example 4
Two – Dimensional Random Variables – Solved Example 5
Two – Dimensional Random Variables – Solved Example 6
Two – Dimensional Random Variables – Solved Example 7
Two – Dimensional Random Variables – Solved Example 8
Two – Dimensional Random Variables – Solved Example 9
Two – Dimensional Random Variables – Solved Example 10
Two – Dimensional Random Variables – Solved Example 11
Random Vectors
Random Vectors – Concept
Function of One Random Variable
Function of One Random Variable – Concept
Function of One Random Variable – Solved Example 1 and 2
Function of One Random Variable – Solved Example 3
Function of One Random Variable – Solved Example 4 and 5
Function of One Random Variable – Solved Example 6
Function of One Random Variable – Solved Example 7
Function of One Random Variable – Solved Example 8 and 9
Function of One Random Variable – Solved Example 10
Function of One Random Variable – Solved Example 11
Function of One Random Variable – Solved Example 12
Function of One Random Variable – Solved Example 13
Function of One Random Variable – Solved Example 14
One Function of Two Random Variables
One Function of Two Random Variables – Result 1, Solved Example 1
One Function of Two Random Variables – Result 1, Solved Example 2
One Function of Two Random Variables – Result 1, Solved Example 3
One Function of Two Random Variables – Result 2, Solved Example 1
One Function of Two Random Variables – Result 3, Solved Example 1
Two Functions of Two Random Variables
Two Functions of Two Random Variables – Concept, Solved Example 1
Two Functions of Two Random Variables – Solved Example 2
Two Functions of Two Random Variables – Solved Example 3
Two Functions of Two Random Variables – Solved Example 4
Two Functions of Two Random Variables – Solved Example 5
Two Functions of Two Random Variables – Solved Example 6
Measures of Central Tendency
Measures of Central Tendency – Concept
Measures of Central Tendency – Solved Example 1
Mathematical Expectations and Moments
Mathematical Expectations and Moments – Concept
Relation Between Central and Non-Central Moments – Concept
Measures of Dispersion
Measures of Dispersion (Quartile Deviation) – Concept
Measures of Dispersion (Quartile Deviation) – Solved Example 1
Measures of Dispersion (Mean Deviation) – Concept
Measures of Dispersion (Standard Deviation and Variance) – Concept
Measures of Dispersion (Standard Deviation and Variance) – Solved Example 1 & 2
Measures of Dispersion (Standard Deviation and Variance) – Solved Example 3
Measures of Dispersion (Standard Deviation and Variance) – Solved Example 4
Measures of Dispersion (Standard Deviation and Variance) – Solved Example 5
Measures of Dispersion (Standard Deviation and Variance) – Solved Example 6
Measures of Dispersion (Standard Deviation and Variance) – Solved Example 7
Measures of Dispersion (Standard Deviation and Variance) – Solved Example 8 & 9
Skewness and Kurtosis
Skewness – Concept
Skewness – Solved Example 1
Kurtosis – Concept
Kurtosis – Solved Example 1
Statistical Averages – Solved Examples
Statistical Averages – Solved Example 1
Statistical Averages – Solved Example 2
Statistical Averages – Solved Example 3
Expected Values of a Two-Dimensional Random Variables
Expected Values of a Two-Dimensional RVs – Concept and Solved Example 1
Expected Values of a Two-Dimensional RVs – Properties
Expected Values of a Two-Dimensional RVs – Solved Example 1
Conditional Expected Values of a Two-Dimensional RVs – Concept
Conditional Expected Values of a Two-Dimensional RVs – Properties
Conditional Expected Values of a Two-Dimensional RVs – Solved Example 1
Conditional Expected Values of a Two-Dimensional RVs – Solved Example 2
Linear Correlation
Linear Correlation – Introduction
Correlation Coefficient
Correlation Coefficient – Concept
Correlation Coefficient – Solved Example 1
Correlation Coefficient – Solved Example 2
Properties of Correlation Coefficient
Properties 1 and 2 of Correlation Coefficient – Concept
Properties 1 and 2 of Correlation Coefficient – Solved Example 1
Properties 1 and 2 of Correlation Coefficient – Solved Example 2
Properties 1 and 2 of Correlation Coefficient – Solved Example 3
Properties 3 and 4 of Correlation Coefficient – Concept
Properties 3 and 4 of Correlation Coefficient – Solved Example 1
Properties 3 and 4 of Correlation Coefficient – Solved Example 2
Properties 3 and 4 of Correlation Coefficient – Solved Example 3
Properties 3 and 4 of Correlation Coefficient – Solved Example 4
Rank Correlation Coefficient
Rank Correlation Coefficient – Concept
Rank Correlation Coefficient – Solved Example 1
Rank Correlation Coefficient – Solved Example 2
Linear Regression
Linear Regression – Introduction
Equations of the Lines of Regression
Equation of the Regression Line of Y on X – Concept
Equation of the Regression Line of X on Y – Concept
Important Notes on Equations of the Regression Lines – Concept
Equations of the Lines of Regression – Solved Example 1
Equations of the Lines of Regression – Solved Example 2
Equations of the Lines of Regression – Solved Example 3
Equations of the Lines of Regression – Solved Example 4
Equations of the Lines of Regression – Solved Example 5
Equations of the Lines of Regression – Solved Example 6
Equations of the Lines of Regression – Solved Example 7
Standard Error of Estimate of Y on X and of X on Y
Standard Error of Estimate of Y on X and of X on Y – Concept
Standard Error of Estimate of Y on X and of X on Y – Solved Example 1
Characteristic Function and Moment Generating Function
Characteristic Function – Definition and Properties – Concept
Characteristic Function – Solved Example 1
Characteristic Function – Solved Example 2
Characteristic Function – Solved Example 3
Characteristic Function – Solved Example 4
Characteristic Function – Solved Example 5
Characteristic Function – Solved Example 6
Characteristic Function – Solved Example 7 and 8
Characteristic Function – Solved Example 9
Moment Generating Function – Definition and Properties – Concept
Moment Generating Function – Solved Example 1
Moment Generating Function – Solved Example 2
Moment Generating Function – Solved Example 3
Moment Generating Function – Solved Example 4
Moment Generating Function – Solved Example 5 and 6
Moment Generating Function – Solved Example 7
Moment Generating Function – Solved Example 8
Moment Generating Function – Solved Example 9
Cumulant Generating Function – Concept and Solved Example 1
Cumulant Generating Function – Solved Example 2
Joint Characteristic Function – Concept and Solved Example 1
Bounds on Probabilities
Tchebycheff Inequality – Concept
Tchebycheff Inequality – Solved Example 1 and 2
Tchebycheff Inequality – Solved Example 3
Tchebycheff Inequality – Solved Example 4
Tchebycheff Inequality – Solved Example 5
Tchebycheff Inequality – Solved Example 6
Tchebycheff Inequality – Solved Example 7
Tchebycheff Inequality – Solved Example 8
Tchebycheff Inequality – Solved Example 9 and 10
Bienayme’s Inequality – Concept
Schwartz Inequality – Concept