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 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 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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What is an Image and How it is Stored on a Computer Author: Bindeshwar Singh Kushwaha Published by: Postnetwork Academy 📸 Concept of an Image An image can be thought of as a 2D function: $$ f(x, y) $$ Each point \( (x, y) \) in the function maps to an intensity or color value.
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📘 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 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 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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📘 Label Encoding and One-Hot Encoding Author: Bindeshwar Singh Kushwaha 🎯 Encoding Categorical Features 🔹 Label Encoding Assigns each category an integer value Suitable for ordinal data (e.g., size: small, medium, large) Tool: LabelEncoder from sklearn.preprocessing Example (Titanic): Encoding Sex as 0 (male), 1 (female) 🔹 One-Hot Encoding Converts categories into binary columns (one per
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K-Nearest Neighbors (KNN) Classifier and Imputation using KNN Author: Bindeshwar Singh Kushwaha What is K-Nearest Neighbors (KNN)? KNN is a supervised machine learning algorithm. It is easy to understand and does not involve complex math. Commonly used for classification tasks, especially with labeled data. We’ll use the Iris dataset, which has flower measurements. Iris Dataset
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Handling Missing Data and Categorical Features By: Bindeshwar Singh Kushwaha Data Preprocessing Flow Raw Data → Handle Missing Values → Encode Categorical Variables → Feature Scaling → Preprocessed Data Overview of Data Preprocessing Load Titanic dataset from CSV file Handle missing values using various techniques Encode categorical data for machine learning Save the cleaned dataset
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