Abdelrahman Amin's repositories
Applying-different-unsupervised-learning-techniques
Applying various clustering techniques to the dataset, and my primary goal is to identify and choose the most effective method that best captures the underlying patterns in the data.
Centroid-in-Pattern_Recognition
This repository implements centroid-based pattern recognition, extracting features from images using grid cell centroids for classification in computer vision and image processing.
Fuzzy-C-Means-Clustering-from-scratch
Fuzzy C-Means (FCM) is a clustering algorithm that assigns membership degrees to data points, allowing for soft assignment to clusters. It offers flexibility, robustness to noise, interpretability, scalability, and versatility in various domains such as pattern recognition and data mining.
Housing-Price
Predicting housing prices with machine learning regression models. This project implements Linear Regression, Random Forest, and Decision Tree models for accurate predictions.
hexapod
Hexapod Robot
Inception-Network-From-Scratch-and-Built_in
Explore the Inception Network, a powerful deep learning architecture designed for image classification. Uncover the efficiency of 1x1 convolutions, strategically used to reduce computational costs and capture intricate features at different scales, revolutionizing the way neural networks process information.
k-nearest-neighbors-KNN-from-scratch-and-Built_in
KNN is a basic machine learning algorithm used for classification and regression tasks. It predicts the class of a new data point based on the majority class of its nearest neighbors. KNN is simple, non-parametric, and learns directly from the training data without explicit training.
Logistic_Regression_From_Scratch
Logistic regression is a statistical technique primarily used for binary classification tasks. It predicts the probability of a binary outcome based on one or more predictor variables. Unlike linear regression, which predicts continuous outcomes, logistic regression deals with categorical outcomes.
Resnet50-From_Scratch_and_Built_in
ResNet-50, with 50 layers, excels in image classification by addressing the vanishing gradient problem. Skip connections facilitate seamless information flow, empowering the model for intricate feature learning. Its unique architecture makes ResNet-50 a robust choice for complex pattern recognition.
Titanic-survival-prediction
The Titanic dataset includes passenger information such as survival status, ticket class, gender, age, family relations aboard, fare, cabin, and port of embarkation. It's widely used for predictive modeling to understand survival patterns based on passenger attributes.
VGG16-From-Scratch-and-Built_in
This project implements the powerful VGG-16 convolutional neural network for image classification, showcasing its efficiency with 3x3 filters, same padding, stride of 1, and 2x2 max-pooling for superior pattern recognition in diverse images.
Web_Scraping-and-Text_Processing-NLP-
Web scraping involves extracting data from websites. Text processing techniques like tokenization, stemming, lemmatization, and removing stopwords refine raw text for analysis.