Machine Learning

Machine Learning Approaches #machinelearningtechniques #mltbcs055

Machine Learning /

# 🌈 Machine Learning Approaches — AKTU BCS-055 🤖 Machine Learning Approaches AKTU BCS-055 — Unit I Artificial Neural Network • Clustering • Reinforcement Learning • Decision Tree • Bayesian Network • SVM • Genetic Algorithm 📌 Introduction Machine Learning can be implemented using different approaches depending on the type of problem, available data, and […]

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Multiple Linear Regression Explained

Artificial Intelligence, Machine Learning /

📈 Multiple Linear Regression Explained PostNetwork Academy 🚀 | Machine Learning Series 📌 What is Regression? Regression is a supervised learning technique where we predict a continuous numerical value instead of a category. Example: predicting house price, temperature, salary, etc. 🏠 House Price Dataset (Multiple Features) Area Bedrooms Age Price (Lakh) 1000 2 5 40

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Loan Approval Dataset से Classification Problem को समझें

Agentic AI, Artificial Intelligence, Machine Learning, Machine Learning /

Loan Approval Dataset से Classification Problem को समझें नमस्कार दोस्तों! PostNetwork Academy में आपका स्वागत है। आज हम Machine Learning की एक बहुत महत्वपूर्ण concept — Classification Problem — को एक simple Loan Approval Dataset की मदद से समझेंगे। 📌 Classification क्या है? Classification एक ऐसी Machine Learning problem है जिसमें model को किसी input

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Machine Learning Techniques

Artificial Intelligence, Deep Learning, Machine Learning, Machine Learning, Probability and Statistics, Projects /

Complete Machine Learning Course (All Lectures + Resources) Unit Lecture Topic Video PDF Quiz Program I L1 Introduction to ML ▶ Video PDF Quiz – I L2 Types of Learning ▶ Video PDF Quiz – I L3 Well-defined Problems ▶ Video PDF Quiz – I L4 Learning System Design ▶ Video PDF Quiz – I

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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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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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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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Learn about the Discrete Uniform Distribution in probability and statistics with detailed explanations, examples, formulas, and visualizations. Understand its mean, variance, and applications such as die rolls and expected frequency calculations. Presented by Bindeshwar Singh Kushwaha, PostNetwork Academy.

Discrete Uniform Distribution in Statistics

Artificial Intelligence, Machine Learning, Probability and Statistics /

Discrete Uniform Distribution By: Bindeshwar Singh Kushwaha PostNetwork Academy Discrete Uniform Distribution A random variable \( X \) is said to have a discrete uniform distribution if it takes integer values from \( a \) to \( b \) with equal probability. The number of possible values is \[ n = b – a +

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