100-Days-Of-ML-Code and 100DaysOfML

These are competing implementations of the same learning curriculum concept, where the first is the established, widely-adopted reference standard (49.8K stars) while the second is a smaller alternative fork attempting to provide more frequent updates and structured projects.

100-Days-Of-ML-Code
51
Established
100DaysOfML
49
Emerging
Maintenance 0/25
Adoption 10/25
Maturity 16/25
Community 25/25
Maintenance 2/25
Adoption 10/25
Maturity 16/25
Community 21/25
Stars: 49,818
Forks: 11,321
Downloads:
Commits (30d): 0
Language:
License: MIT
Stars: 175
Forks: 39
Downloads:
Commits (30d): 0
Language: Jupyter Notebook
License: GPL-3.0
Stale 6m No Package No Dependents
Stale 6m No Package No Dependents

About 100-Days-Of-ML-Code

Avik-Jain/100-Days-Of-ML-Code

100 Days of ML Coding

Structured curriculum covering foundational ML algorithms (regression, classification, SVM, decision trees) paired with mathematical prerequisites via curated video resources and infographics. Includes hands-on implementations using scikit-learn and Python across supervised learning techniques, complemented by deep learning specialization coursework and theoretical foundations from university-level lectures. Progressively builds from data preprocessing fundamentals through advanced topics like kernel methods and neural networks, with accompanying datasets and code examples for each concept.

About 100DaysOfML

lucifertrj/100DaysOfML

100 Days Of Machine Learning. New Content in every 1-2 day and projects every week. The massive 100DaysOfML in building

Scores updated daily from GitHub, PyPI, and npm data. How scores work