Machine Learning

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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Iris Classification Neural Network with Backpropagation

Artificial Intelligence, Machine Learning /

      Iris Classification Neural Network with Backpropagation Bindeshwar Singh Kushwaha PostNetwork Academy Forward Propagation Step 1 Dataset features: \( x_1 = \text{Sepal length}, \; x_2 = \text{Sepal width}, \; x_3 = \text{Petal length}, \; x_4 = \text{Petal width} \) Forward Propagation Step 2 \( z_{h1} = w_{11}x_1 + w_{21}x_2 + w_{31}x_3 + w_{41}x_4

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Spectrogram of Speech in Python

Artificial Intelligence, Machine Learning /

  Spectrogram of Speech Author: Bindeshwar Singh Kushwaha — PostNetwork Academy What is a Spectrogram? Spectrogram — a visual representation of sound. Shows how the frequency content of a signal changes over time. Axes of a spectrogram: X-axis: Time (seconds) Y-axis: Frequency (Hz) Color / Intensity: Amplitude or Power (dB) Computed using the Short-Time Fourier

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Reading, Saving, and Displaying an Image in Python

Artificial Intelligence, Machine Learning, Natural Language Processing /

Reading, Saving, and Displaying an Image in Python Author: Bindeshwar Singh Kushwaha – Postnetwork Academy Introduction In Python, working with images is a common requirement for data science, computer vision, and AI projects. Images can be loaded, manipulated, and displayed using various libraries such as matplotlib, scikit-image, and OpenCV. This tutorial demonstrates how to read,

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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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Linear Regression using Gradient Descent

Artificial Intelligence, Machine Learning /

  Linear Regression using Gradient Descent By Bindeshwar Singh Kushwaha General Linear Regression Model We have a collection of labeled examples: $$ \{(\mathbf{x}_i, y_i)\}_{i=1}^{N} $$ \( \mathbf{x}_i \) is a \( D \)-dimensional feature vector \( y_i \) is a real-valued target Each feature \( x_i^{(j)} \in \mathbb{R} \), where \( j = 1, …,

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