Data Science

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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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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Naive Bayes Classification Algorithm for Weather Dataset

Artificial Intelligence, Machine Learning, Machine Learning /

Naive Bayes Classification Algorithm for Weather Dataset Author: Bindeshwar Singh Kushwaha | PostNetwork Academy Introduction to Naive Bayes Classifier Naive Bayes is a probabilistic classification algorithm. It is based on Bayes’ Theorem and the naive independence assumption. Suppose we have a feature vector \(\mathbf{X} = (x_1, x_2, …, x_n)\) and a class \(y\). Bayes Theorem:

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Text Classification with Bag of Words and Naive Bayes

Data Handling, Machine Learning, Statistics /

Text Classification with Bag of Words and Naive Bayes Author: Bindeshwar Singh Kushwaha | PostNetwork Academy Understanding Text with Machine Learning Processing and understanding text allows extraction of meaningful information from raw data. Text data can be structured into features that machine learning algorithms can analyze. Machine learning approaches include supervised, unsupervised, and deep learning

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Gradient of Softmax + Cross-Entropy w.r.t Logits

Artificial Intelligence, Deep Learning, Machine Learning, Machine Learning /

Gradient of Softmax + Cross-Entropy w.r.t Logits Author: Bindeshwar Singh Kushwaha – PostNetwork Academy Goal We want to compute: $$ \frac{\partial L}{\partial z_j} $$ Notation: Logits: \(z = [z_1, z_2, \dots, z_C]\) Softmax: \(\hat{y}_i = \frac{e^{z_i}}{\sum_{k=1}^{C} e^{z_k}}\) Cross-Entropy Loss: \(L = -\sum_{i=1}^{C} y_i \log \hat{y}_i\), where \(y_i\) is one-hot. [Insert Neural Network Diagram Here] Loss

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Understanding Neural Networks: Softmax, Cross-Entropy, and Backpropagation

Deep Learning, Machine Learning /

Understanding Neural Networks: Softmax, Cross-Entropy, and Backpropagation Author: Bindeshwar Singh Kushwaha – PostNetwork Academy Neural Network with Softmax + Cross-Entropy Input Layer: The network receives 3 input features, denoted \(x_1, x_2, x_3\). Hidden Layer: 2 neurons in the hidden layer with activations \(a^{(1)}\) and \(a^{(2)}\). Output Layer: 2 outputs \(z^{(3)}, z^{(4)}\), passed through softmax. Softmax

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Poisson Distribution Numerical Examples

Artificial Intelligence, Machine Learning, Probability and Statistics /

📘 Poisson Distribution Numerical Examples Author: Bindeshwar Singh Kushwaha Institution: PostNetwork Academy Example 1: Truck Arrivals The number of heavy trucks arriving at a railway station follows a Poisson distribution with an average of 2 arrivals per hour. Find: (a) Probability that no truck arrives (b) Probability that at least two trucks arrive Let \(

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Feature Scaling in Machine Learning : Preprocessing Technique

Artificial Intelligence, Data Handling, Machine Learning, Research and Development /

Feature Scaling in Machine Learning Author: Bindeshwar Singh Kushwaha Postnetwork Academy Why Feature Scaling? Machine learning algorithms often struggle when input features have different scales. Example: Total number of rooms might range from 6 to 39,320, while median incomes range from 0 to 15. Two common methods to scale features: Min-Max Scaling (Normalization) Standardization Min-Max

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