Introduction To Applied Probability

What you'll learn
Basic Definitions related to Probability Theory
Mathematical Definition of Probability
Important Symbols and Results related to Probability Theory
Conditional Probability
Theorem of Total Probability
Baye's Theorem
Bernoulli's Trials
Probability of Uncountable Uniform Spaces
Requirements
Fundamental of Maths
Basic Arithmetics
Knowledge of Permutations and Combinations
Description
HOW INTRODUCTION TO APPLIED PROBABILITY IS SET UP TO MAKE COMPLICATED PROBABILITY AND STATISTICS EASYThis course deals with concepts required for the study of Machine Learning and Data Science. Statistics is a branch of science that is an outgrowth of the Theory of Probability. Probability & Statistics are used in Machine Learning, Data Science, Computer Science and Electrical Engineering.This 35+ lecture course includes video explanations of everything from Fundamental of Probability, and it includes more than 35+ examples (with detailed solutions) to help you test your understanding along the way. Introduction To Applied Probability is organized into the following sections:IntroductionSome Basic DefinitionsMathematical Definition of ProbabilitySome Important SymbolsImportant ResultsConditional ProbabilityTheorem of Total ProbabilityBaye's TheoremBernoulli's TrialsUncountable Uniform Spaces
Overview
Section 1: Introduction
Lecture 1 Why do we need Probability
Section 2: Some Basic Definitions
Lecture 2 Some Basic Definitions - Part 1
Lecture 3 Some Basic Definitions - Part 2
Lecture 4 Some Basic Definitions - Part 3
Lecture 5 Some Basic Definitions - Part 4
Section 3: Mathematical Definition of Probability
Lecture 6 Probability - Definition and Solved Example 1, 2 and 3
Lecture 7 Probability - Definition and Solved Example 4
Lecture 8 Probability - Definition and Solved Example 5 and 6
Lecture 9 Probability - Definition and Solved Example 7
Section 4: Some Important Symbols
Lecture 10 Some Important Symbols
Section 5: Important Results
Lecture 11 Important Results - Concept and Solved Example 1
Lecture 12 Important Results - Solved Example 2
Lecture 13 Important Results - Solved Example 3
Lecture 14 Important Results - Solved Example 4
Lecture 15 Important Results - Solved Example 5 and 6
Lecture 16 Important Results - Solved Example 7
Section 6: Conditional Probability
Lecture 17 Conditional Probability - Concept and Solved Example 1
Lecture 18 Conditional Probability - Solved Example 2
Lecture 19 Conditional Probability - Solved Example 3
Section 7: Theorem of Total Probability
Lecture 20 Theorem of Total Probability - Concept and Solved Example 1
Lecture 21 Theorem of Total Probability - Solved Example 2
Lecture 22 Theorem of Total Probability - Solved Example 3
Lecture 23 Theorem of Total Probability - Solved Example 4
Section 8: Baye's Theorem
Lecture 24 Baye's Theorem - Concept and Solved Example 1
Lecture 25 Baye's Theorem - Solved Example 2
Lecture 26 Baye's Theorem - Solved Example 3
Lecture 27 Baye's Theorem - Solved Example 4
Section 9: Bernoulli's Trials
Lecture 28 Bernoulli's Trials - Concept and Solved Example 1 and 2
Lecture 29 Bernoulli's Trials - Solved Example 3
Lecture 30 Bernoulli's Trials - Solved Example 4
Lecture 31 Bernoulli's Trials - Solved Example 5
Lecture 32 Bernoulli's Trials - Solved Example 6
Lecture 33 Bernoulli's Trials Generalisation- Concept and Solved Example 1
Section 10: Uncountable Uniform Spaces
Lecture 34 Uncountable Uniform Spaces - Solved Example 1
Lecture 35 Uncountable Uniform Spaces - Solved Example 2
Current Probability and Statistics students, or students about to start Probability and Statistics who are looking to get ahead,Students of Machine Learning, Data Science, Computer Science, Electrical Engineering , as Probability is the prerequisite course to Machine Learning, Data Science, Computer Science and Electrical Engineering,Anyone who wants to study Probability for fun after being away from school for a while.

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