Bindeshwar S. Kushwaha

Normal Distribution – Numerical Problems with Solutions

Artificial Intelligence, Machine Learning, Probability and Statistics /

Normal Distribution – Numerical Problems with Solutions Author: Bindeshwar Singh Kushwaha Platform: PostNetwork Academy 1. Definition of Normal Distribution A continuous random variable $X$ follows a Normal Distribution with mean $\mu$ and variance $\sigma^2$ if its probability density function (PDF) is: $$ f(x) = \frac{1}{\sigma \sqrt{2\pi}} e^{ -\frac{(x – \mu)^2}{2\sigma^2} }, \quad -\infty < x […]

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Retrieval-Augmented Generation (RAG) with LLMs

Retrieval-Augmented Generation (RAG)Retrieval-Augmented Generation (RAG) Explained | LLMs and the Need for RAG

Agentic AI, Generative A. I., Large Language Models, RAG /

Retrieval-Augmented Generation (RAG) LLMs and the Need for RAG Author: Bindeshwar Singh Kushwaha Platform: PostNetwork Academy Let’s Start With a Question Suppose you ask a Large Language Model: “What is the medical history of my uncle?” Will the LLM know the answer? Answer: No. Why Can’t the LLM Answer? LLMs are trained on public Internet

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Python for A.I. (ML DL GENAI LLMS AGENTIC AI ) and ROBOTICS

Uncategorized /

No. Topic Complete (Post + Video + PDF + Quiz) 1 Python Programming Basics 1.1 Introduction to Python & Installation 🔗 1.2 Variables, Data Types & Operators 🔗 1.3 Conditional Statements 🔗 1.4 Loops (for, while) 🔗 1.5 Functions & Recursion 🔗 1.6 Lists, Tuples, Sets, Dictionaries 🔗 1.7 File Handling & Exception Handling 🔗

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Normal Distribution A Detailed Step-by-Step Explanation

Artificial Intelligence, Machine Learning, Machine Learning, Mathematics, Probability and Statistics, Research and Development, Statistics /

Normal Distribution A Detailed Step-by-Step Explanation By Bindeshwar Singh Kushwaha PostNetwork Academy Introduction: Random Variables A random variable (r.v.) is a function that assigns a numerical value to each outcome of a random experiment. There are two main types of random variables: Discrete Random Variable: Takes countable values (e.g., number of heads in 3 coin

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Negative Binomial Distribution | Simple Explanation #177 Data Sc. and A.I. Lect. Series

Probability and Statistics, Statistics /

Negative Binomial Distribution A Detailed Step-by-Step Explanation By Bindeshwar Singh Kushwaha PostNetwork Academy Introduction: Relation with Geometric Distribution The negative binomial distribution is a generalization of the geometric distribution. It describes the number of failures before the \( r^{th} \) success in a sequence of Bernoulli trials. When \( r = 1 \), it reduces

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Support Vector Machines Made Easy | SVM Explained with Example

Artificial Intelligence, Machine Learning, Machine Learning /

Support Vector Machine (SVM) A Simple Numerical Example – Detailed Explanation Author: Bindeshwar Singh Kushwaha PostNetwork Academy Introduction: Type and Purpose of SVM Type of Algorithm: Supervised Machine Learning Algorithm Used for Classification and Regression (SVR) Discriminative Model – finds decision boundaries Known as a Maximum-Margin Classifier Purpose: Find the optimal hyperplane that separates classes

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Geometric Distribution Made Simple | Stepwise Approach #176 Data Sc. and A.I. Lect. Series

Algebra, Artificial Intelligence, Machine Learning, Probability and Statistics /

  Geometric Distribution Made Simple | Stepwise Approach Bindeshwar Singh Kushwaha PostNetwork Academy  Geometric Distribution Let a sequence of Bernoulli trials be performed, each with constant probability \(p\) of success and \(q = 1 – p\) of failure. Trials are independent, and we continue performing them until the first success occurs. Let \(X\) be the

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Hypergeometric Distribution A Distribution of Dependent Events #175 Data Sc. and A.I. Lect. Series

Artificial Intelligence, Machine Learning, Probability and Statistics, Research and Development /

    Hypergeometric Distribution : A Distribution of Dependent Events By Bindeshwar Singh Kushwaha PostNetwork Academy Introduction In the previous sections, we studied distributions such as the binomial distribution. The binomial distribution assumes that each trial is independent and the probability of success remains constant. However, in many real-life problems, selections are made without replacement.

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