Machine Learning

Spectrogram of Speech in Python

  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

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

📘 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

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

  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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