Скачать или смотреть Types of Probability Distribution | What is Discrete & Continuous Probability Distribution

Types of Probability Distribution | Discrete Probability Distribution & Continuous Probability Distribution
🔥Liverpool John Moore University MS In Data Science: https://www.upgrad.com/data-science-m...
🔥IIIT Bangalore Post Graduate Program in Data Science & AI: https://www.upgrad.com/data-science-p...
🔥Master of Science in Machine Learning & AI: https://www.upgrad.com/masters-in-ml-...
🔥Post Graduate Certificate in Machine Learning and Deep Learning: https://www.upgrad.com/machine-learni...

Probability distribution is a statistical function that describes the likelihood of different values or ranges of values that a random variable can take. It tells us how probable it is for a variable to take on certain values or fall within certain ranges.
Hello and welcome to a Data Science Tutorial for Beginners. In this video we will walk you through the topic 'What is Probability Distribution' & types of probability distribution that are nothing but discrete probability distribution & Continuous probability distribution.
Understanding probability distributions is fundamental to mastering machine learning, especially for beginners venturing into Data Science.

What Is A Discrete Probability Distribution?
A discrete probability distribution assigns probabilities to each possible outcome of a discrete random variable, which can take on a finite or countably infinite set of values. Discrete variables have distinct, separate values. For example, the number of heads in multiple coin flips or the count of defective items in a batch are discrete random variables.

What Is A Continuous Probability Distribution?
A continuous probability distribution describes the probabilities of a continuous random variable. Unlike discrete random variables, which can only take on specific, distinct values (like the number of heads in coin flips), a continuous random variable can take on any value within a given range.

Understanding discrete probability distributions is essential for evaluating machine learning models and making data-driven decisions. In natural language processing, they model word frequencies, while in recommendation systems, they predict user preferences. Mastering these concepts enables beginners to effectively implement machine learning algorithms. This knowledge enhances the ability to model and interpret data, leading to more accurate and reliable models.

Topics Covered
Chapter 1 - Discrete Probability Distribution
00:00:39 - Fundamentals Of Probability Distribution
00:00:43 - Discrete Probability Distribution
00:01:45 - Why Probability?
00:03:40 - Probability and Data Science
00:05:22 - Some Applications of Probability
00:07:46 - Some Terminology
00:15:35 - What is Frequency Distribution?
00:18:56 - Definitions of Probability
00:21:00 - What is Relative Frequency?
00:24:17 - Empirical Vs Theoretical Probability
00:25:52 - Interpretation Of Probability
00:28:43 - Empirical and Theoretical Distributions
00:33:36 - Why Empirical and Theoretical Distributions is Important?
00:36:48 - Discrete Random Variables
00:37:56 - What is A Random Variable?
00:40:41 - Probability Mass Function
00:48:41 - What is Expected Value?
01:00:31 - Variance and Standard Deviation
01:12:07 - What is Uniform Distribution?

Chapter 2 - Continuous Probability Distribution
01:18:12 - What is Continuous Probability Distribution?
01:19:51 -Types of Random Variables
01:21:28 - Continuous Random Variables
01:25:07 - Frequency Distribution
01:28:53 - Advantages and Disadvantages of Frequency Distribution Method
01:31:34 - What is Continuous Probability Distributions?
01:50:18 - The Normal Or The Gaussian Distribution
02:02:36 - Implication Of Normal Distributed Data
02:09:04 - Standardization of Normal Distributed Data
02:12:38 - What is Z-Table?
02:25:48 - Examples on Normal Distributed Data

#probabilitydistribution #probability_distribution

Let’s keep up our progress and build our knowledge step-by-step!

Subscribe, hit the bell, and I'll see you in the next step of our machine learning journey.