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 EASY

This 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:

Introduction

Some Basic Definitions

Mathematical Definition of Probability

Some Important Symbols

Important Results

Conditional Probability

Theorem of Total Probability

Baye's Theorem

Bernoulli's Trials

Uncountable Uniform Spaces

Who this course is for:

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.

Homepage: https://www.udemy.com/course/probability-and-statistics-for-machine-learning-1/

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