100-Days-Of-ML-Code open source analysis

100 Days of ML Coding

Project overview

⭐ 49180 · Last activity on GitHub: 2023-12-29

GitHub: https://github.com/Avik-Jain/100-Days-Of-ML-Code

Why it matters for engineering teams

100-Days-Of-ML-Code addresses the practical challenge of building foundational machine learning skills through hands-on implementation. It is well suited for machine learning and AI engineering teams looking to deepen their understanding of core algorithms and techniques in a structured way. While the project is mature as a learning resource and offers reliable code examples, it is not designed as a production ready solution or a self hosted option for direct deployment. Teams seeking scalable, production-grade machine learning frameworks should consider more specialised libraries. This repository is ideal for engineers focused on skill development rather than immediate integration into live systems.

When to use this project

This project is a strong choice for teams aiming to build foundational knowledge in machine learning through practical exercises and tutorials. It is less suitable when the goal is to deploy production ready models quickly or to integrate with existing enterprise pipelines, where more robust frameworks would be preferable.

Team fit and typical use cases

Machine learning engineers and AI specialists benefit most from this open source tool for engineering teams by using it to reinforce algorithmic concepts and implementation skills. It typically supports product teams working on proof of concepts or prototypes involving supervised learning techniques. The repository’s focus on core algorithms makes it a valuable resource for educational purposes within data-driven product development.

Best suited for

Machine Learning and AI Engineer

Topics and ecosystem

100-days-of-code-log 100daysofcode deep-learning implementation infographics linear-algebra linear-regression logistic-regression machine-learning machine-learning-algorithms naive-bayes-classifier python scikit-learn siraj-raval siraj-raval-challenge support-vector-machines svm tutorial

Activity and freshness

Latest commit on GitHub: 2023-12-29. Activity data is based on repeated RepoPi snapshots of the GitHub repository. It gives a quick, factual view of how alive the project is.