# How is your knowledge in deep learning?
I possess a solid interest in solving scientific and real-life problems by building and testing
machine learning (ML)-based modules and testing them in reality. My knowledge in deep
learning is based on practical experience gained through completing several projects and out of
university coursework.
# What are the courses you have taken in deep learning?
I have completed a 93-hour Career Track in Machine Learning offered by DataCamp:
https://app.datacamp.com/ . This course covered various topics in machine learning, including
supervised learning, unsupervised learning, deep learning, and neural networks.
# What are the different related projects?
Project 1
Title: Maximizing Tidal Stream Energy Generation with a Spatially-Aware Learning System
for Optimal Localization
The project integrates graph theory-based algorithms with deep learning-based algorithms to
identify the optimal configuration of turbines for maximum energy generation. The system
predicts tidal stream flow data and utilizes virtualized heat maps to determine the best locations
for placing tidal turbines. The process involves mapping 11 matrices onto virtualized locations
and identifying the best locations based on energy generation potential using clustering
techniques or other deep learning algorithms.
Tools used: K-means Clustering, Neural Network, Minimum Spanning Tree (MST)
Algorithm, Matrices and heat maps data with realistic randomization.
(On Progress and Developments)
Project 2
Title: ML-Based Strategies for Reducing Traffic Mortality
The ML-Based Strategies for Reducing Traffic Mortality project analyzed traffic mortality rates
in the US using data from the National Highway Traffic Safety Administration. The project
involved data overview, textual and graphical summaries, multivariate linear regression, PCA,
K-Means clustering, and feature visualization. The project aimed to identify strategies for
reducing traffic mortality rates.
Tools used: Multivariate Linear Regression, PCA, K-Means clustering.
GitHub: https://github.com/A7MED73/-Reducing-TrafficMortality/tree/main/Reducing%20Traffic%20Mortality
Project 3
Title: ML-Assisted Loading and Processing of Images for Honeybee and Bumblebee
Detection.
The ML-assisted Loading and Processing Images for Honeybees and Bumblebees Detection
project aims to detect honeybees and bumblebees in images using a convolutional neural
network (CNN). The images are pre-processed through cropping, resizing, and normalizing to
improve the model's accuracy. The project's results will aid researchers in understanding bee
populations and behavior.
Tools used: PIL, CNN, Color Channels, Image Processing Pipeline.
GitHup: https://github.com/A7MED73/ImageProcessing
Project 4
Title: ML-Assisted Malware Analysis System
The goal of this project is to build a complete ML-assisted malware analysis system that can
detect, analyze, and respond to malicious activity. The system should be able to detect malware
in files, analyze the code for malicious behavior, and respond appropriately.
Tools used: MalwareBazaar repository, Support Vector Machines (SVMs), Random Forests
(RFs),
(On Progress and Developments)