Vaamanikam11

Vaamanikam11's Stars

• krishnaik06/Roadmap-To-Learn-Generative-AI-In-2024

  3.4k1.5k

• krishnaik06/The-Grand-Complete-Data-Science-Materials

  Language:Python7.1k3.7k

• ashishpatel26/500-AI-Machine-learning-Deep-learning-Computer-vision-NLP-Projects-with-code

  500 AI Machine learning Deep learning Computer vision NLP Projects with code

  21.4k5.3k

• skillsire/Daily-H1B-Jobs

  H1B & other VISA Sponsored Jobs List Updated Daily

  977

• MilovanTomasevic/iOS-Swift-The-Complete-iOS-App-Development-Bootcamp

  From Beginner to iOS App Developer with Just One Course! Fully Updated with a Comprehensive Module Dedicated to SwiftUI!

  Language:Swift7419

• BoostIO/BoostNote-App

  Boost Note is a document driven project management tool that maximizes remote DevOps team velocity.

  Language:TypeScript3.8k372

• yangshun/tech-interview-handbook

  💯 Curated coding interview preparation materials for busy software engineers

  Language:TypeScript120k14.9k

• jwasham/coding-interview-university

  A complete computer science study plan to become a software engineer.

  309k77.4k

• krishnadey30/LeetCode-Questions-CompanyWise

  Contains Company Wise Questions sorted based on Frequency and all time

  12.9k4.1k

• ashishps1/awesome-leetcode-resources

  Awesome LeetCode resources to learn Data Structures and Algorithms and prepare for Coding Interviews.

  Language:Java6.7k1.8k

• zslucky/awesome-AI-books

  Some awesome AI related books and pdfs for learning and downloading, also apply some playground models for learning

  Language:Jupyter Notebook1.3k328

• cslegasse/CS-Tech-Resource-Hub

  A github of all CS related resources!

  Language:SCSS40118

• MiltonPereiraNeto/Asabeneh-30-Days-Of-Python

  Language:Python68274

• Jared-Chan/f1ml

  Formula One Race Lap-by-Lap Prediction with Machine Learning

  Language:Jupyter Notebook16224

• codecrafters-io/build-your-own-x

  Master programming by recreating your favorite technologies from scratch.

  Language:Markdown322k29.9k

• KushalVijay/KushalVijay

  125

• SimplifyJobs/Summer2025-Internships

  Collection of Summer 2025 tech internships!

  35.6k2.8k

• rajaprerak/rajaprerak.github.io

  Personal Portfolio Website

  Language:HTML933420

• rustformers/llm

  [Unmaintained, see README] An ecosystem of Rust libraries for working with large language models

  Language:Rust6.1k369

• riti2409/Resources-for-preparation-Of-Placements

  Lecture video links for preparation of Placements

  3.6k901

• kananinirav/AWS-Certified-Cloud-Practitioner-Notes

  AWS Certified Cloud Practitioner Short Notes And Practice Exams (CLF-C02)

  Language:HTML2.1k755

• kennethleungty/AWS-Certified-Cloud-Practitioner-Notes

  Notes compiled based on AWS E-Learning lessons and transcripts

  907360

• naviatolin/Autem

  Productivity application that takes into account your mental health written in python using Flask.

  Language:HTML1

• lindsey-h/Project-Elevate

  Elevate is a web application written in Python and JavaScript that gives people struggling with mental health an easy way to reach out to their family and friends when they need support the most. They simply supply their availability and press a single button to alert their contacts via the Twilio API. Uses the Evo Calendar library.

  Language:JavaScript2

• thuo-huynh/aws-cloud-practioner-notes

  11

• wshahbaz/transformer_translator

  Transformer model to translate Portugese to English with high accuracy

  Language:Jupyter Notebook1

• sankaraJ/Multivariate-Linear-Regression

  Analyzing the Loan Prediction Dataset using the multivariate linear regression from scratch

  38

• omrawal/Chatbot-API

  API for Mental Health Bot Chatbot

  Language:Python96

• lepisma/mento

  Mento is a program for tracking mental health

  Language:Python214

• Shauqi/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.

  Language:Jupyter Notebook4521