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Student @ JKUAT
- Nairobi, Kenya
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14:12
(UTC +03:00) - https://leetcode.com/u/muthoniWachira/
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Curated coding interview preparation materials for busy software engineers
A curated list of awesome Machine Learning frameworks, libraries and software.
The definitive list that answers "I want to do X in Python, which tool should I use?"
A Collection of application ideas which can be used to improve your coding skills.
🎓 Path to a free self-taught education in Computer Science!
📚 Freely available programming books
Clean Code concepts adapted for JavaScript
A book series (2 published editions) on the JS language.
Learn how to design large-scale systems. Prep for the system design interview. Includes Anki flashcards.
📝 Algorithms and data structures implemented in JavaScript with explanations and links to further readings
A complete computer science study plan to become a software engineer.
Interactive roadmaps, guides and other educational content to help developers grow in their careers.
500 AI Machine learning Deep learning Computer vision NLP Projects with code
Curated list of project-based tutorials
The 30 Days of Python programming challenge is a step-by-step guide to learn the Python programming language in 30 days. This challenge may take more than 100 days. Follow your own pace. These vide…
This is a node js http listener for callback and timeout urls for Safaricom's M-Pesa APIs.
M-Pesa Library for Node.js using REST API
InterviewX is an AI-powered interview practice platform that helps users prepare smarter through real-time mock interviews, technical assessments, and performance insights. Built with a modern full…
A simple Etch-a-Sketch drawing pad built with HTML/CSS/JavaScript.
A small web project for exploring country data.
An interactive git visualization and tutorial. Aspiring students of git can use this app to educate and challenge themselves towards mastery of git!
Implementation of a binary search tree from sorted array using javascript
This repo focuses on the fundamentals of machine learning, covering core concepts, model evaluation, and the practical challenges of communicating model performance to non-technical stakeholders.