Pinned Repositories
500-AI-Machine-learning-Deep-learning-Computer-vision-NLP-Projects-with-code
500 AI Machine learning Deep learning Computer vision NLP Projects with code
AI-Expert-Roadmap
Roadmap to becoming an Artificial Intelligence Expert in 2022
Analysis-of-Vaccination-Effect-on-Covid-19-Cases-all-over-the-World
Advanced Machine Learning Course Project
Attack-and-Anomaly-Detection-in-IoT-Sensors-in-IoT-Sites-Using-Machine-Learning-Approaches
Attack and Anomaly detection in the Internet of Things (IoT) infrastructure is a rising concern in the domain of IoT. With the increased use of IoT infrastructure in every domain, threats and attacks in these infrastructures are also growing commensurately. Denial of Service, Data Type Probing, Malicious Control, Malicious Operation, Scan, Spying and Wrong Setup are such attacks and anomalies which can cause an IoT system failure. In this paper, performances of several machine learning models have been compared to predict attacks and anomalies on the IoT systems accurately. The machine learning (ML) algorithms that have been used here are Logistic Regression (LR), Support Vector Machine (SVM), Decision Tree (DT), Random Forest (RF), and Artificial Neural Network (ANN). The evaluation metrics used in the comparison of performance are accuracy, precision, recall, f1 score, and area under the Receiver Operating Characteristic Curve. The system obtained 99.4% test accuracy for Decision Tree, Random Forest, and ANN. Though these techniques have the same accuracy, other metrics prove that Random Forest performs comparatively better.
awesome-leetcode-resources
This repository contains LeetCode articles which are very useful during the interview preparation.
awesome-public-datasets
A topic-centric list of HQ open datasets. PR ☛☛☛
awesome-python
A curated list of awesome Python frameworks, libraries, software and resources
awesome-r-learning-resources
A curated collection of free resources to help deepen your understanding of the R programming language. Updated regularly. Contributions encouraged via pull request (see contributing.md).
battleship
2-2 Java project
Compiler-design-with-flex-and-bison
ishrak-zarif's Repositories
ishrak-zarif/500-AI-Machine-learning-Deep-learning-Computer-vision-NLP-Projects-with-code
500 AI Machine learning Deep learning Computer vision NLP Projects with code
ishrak-zarif/AI-Expert-Roadmap
Roadmap to becoming an Artificial Intelligence Expert in 2022
ishrak-zarif/Analysis-of-Vaccination-Effect-on-Covid-19-Cases-all-over-the-World
Advanced Machine Learning Course Project
ishrak-zarif/Attack-and-Anomaly-Detection-in-IoT-Sensors-in-IoT-Sites-Using-Machine-Learning-Approaches
Attack and Anomaly detection in the Internet of Things (IoT) infrastructure is a rising concern in the domain of IoT. With the increased use of IoT infrastructure in every domain, threats and attacks in these infrastructures are also growing commensurately. Denial of Service, Data Type Probing, Malicious Control, Malicious Operation, Scan, Spying and Wrong Setup are such attacks and anomalies which can cause an IoT system failure. In this paper, performances of several machine learning models have been compared to predict attacks and anomalies on the IoT systems accurately. The machine learning (ML) algorithms that have been used here are Logistic Regression (LR), Support Vector Machine (SVM), Decision Tree (DT), Random Forest (RF), and Artificial Neural Network (ANN). The evaluation metrics used in the comparison of performance are accuracy, precision, recall, f1 score, and area under the Receiver Operating Characteristic Curve. The system obtained 99.4% test accuracy for Decision Tree, Random Forest, and ANN. Though these techniques have the same accuracy, other metrics prove that Random Forest performs comparatively better.
ishrak-zarif/awesome-leetcode-resources
This repository contains LeetCode articles which are very useful during the interview preparation.
ishrak-zarif/awesome-public-datasets
A topic-centric list of HQ open datasets. PR ☛☛☛
ishrak-zarif/awesome-python
A curated list of awesome Python frameworks, libraries, software and resources
ishrak-zarif/awesome-r-learning-resources
A curated collection of free resources to help deepen your understanding of the R programming language. Updated regularly. Contributions encouraged via pull request (see contributing.md).
ishrak-zarif/battleship
2-2 Java project
ishrak-zarif/Compiler-design-with-flex-and-bison
ishrak-zarif/free-programming-books
:books: Freely available programming books
ishrak-zarif/graphrag-accelerator
One-click deploy of a Knowledge Graph powered RAG (GraphRAG) in Azure
ishrak-zarif/ishrak-zarif
My personal repository.
ishrak-zarif/machine-learning-coursera-1
This repo is specially created for all the work done my me as a part of Coursera's Machine Learning Course.
ishrak-zarif/Machine-Learning-Lab
ishrak-zarif/Modelling_And_Simulation
ishrak-zarif/OpenGL
OpenGL 3 and 4 with GLSL
ishrak-zarif/OpenGL-Computer-Graphics-Project
ishrak-zarif/OpenGl3D
ishrak-zarif/Opinion-mining-and-sentiment-analysis
ishrak-zarif/postal
ishrak-zarif/Programming-for-Statistics-and-Data-Science
Programming for Statistics and Data Science, published by Packt
ishrak-zarif/Prompt-Engineering-Guide
🐙 Guides, papers, lecture, notebooks and resources for prompt engineering
ishrak-zarif/Restaurant-Website-in-php
ishrak-zarif/roadmaps
This repo is to add pages on various career paths and roadmaps such as data scientist, software engineer etc.
ishrak-zarif/Ruby-on-rails---simple-overview
ishrak-zarif/Software-company-database
ishrak-zarif/Spark-The-Definitive-Guide
Spark: The Definitive Guide's Code Repository
ishrak-zarif/tensorflow
Computation using data flow graphs for scalable machine learning
ishrak-zarif/Twitter-Sentiment-Analysis-with-NLTK