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Bivariate Discrete Random Variables Data Science and A.I. Lecture Series By Bindeshwar Singh Kushwaha, PostNetwork Academy Definition Let \( X \) and \( Y \) be two discrete random variables defined on the sample space \( S \) of a random experiment. Then, the function \( (X, Y) \) defined on the same sample space […]
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Definition: Continuous CDF A continuous random variable can take an infinite number of values in a given range. The Probability Density Function (PDF) \( f(x) \) describes the likelihood of \( X \) falling within a small interval. The Cumulative Distribution Function (CDF) is given by: \[ F(x) = P[X \leq x] = \int_{-\infty}^{x} f(t)
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Learn about Ordinary Least Squares (OLS) Regression with a step-by-step mathematical derivation, visualization, and real-world dataset example. Understand how to compute regression coefficients, fit the best line, and make predictions using the OLS method. Perfect for students, researchers, and data science enthusiasts!
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Definition of Differential Equations A differential equation is an equation that involves one or more derivatives of an unknown function. Example: \[ \frac{dy}{dx} = 3x^2 \] Types of Differential Equations Ordinary Differential Equations (ODEs): \( \frac{dy}{dx} + 2y = x^2 \) Partial Differential Equations (PDEs): \( \frac{\partial u}{\partial x} + \frac{\partial u}{\partial y} = 0
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Learn about Tokens in Python, the fundamental building blocks of Python programming. Explore keywords, identifiers, literals, operators, and punctuators with detailed explanations. Perfect for beginners in data science and AI
Learn about Python expressions with this comprehensive guide! Understand how operators, precedence, and evaluation rules work with real examples. Perfect for beginners and developers. Explore now
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Introduction to Vectors A vector is a mathematical object that has both magnitude and direction. Vectors are essential in physics, engineering, and mathematics. They can be represented in different dimensions, such as real number space \(\mathbb{R}^n\) and complex number space \(\mathbb{C}^n\).
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Supervised Learning in Machine Learning Introduction Supervised learning is a machine learning technique where a model learns from labeled data. The dataset consists of input features \((x_1, x_2, x_3)\) and an output label \(y\). The goal is to find a function that maps inputs to correct outputs.
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Welcome to another insightful lesson from PostNetwork Academy. In this video, Bindeshwar Singh Kushwaha explores the concept of probability, focusing on the probability of at least one independent event happening. This topic is essential for students and professionals in Data Science, AI, or Mathematics. What You’ll Learn in This Video: – Formula for the probability
Explore the concepts of Conditional Probability and the Multiplicative Law with real-world examples and engaging explanations. This lecture, part of the “Data Science and A.I. Lecture Series” by Bindeshwar Singh Kushwaha from PostNetwork Academy, covers: 1. Conditional Probability: Learn how to calculate the likelihood of an event occurring, given that another event has already happened,