100-Days-Of-ML-Code and 50-Days-of-ML
These are competitors offering alternative structured learning curricula for machine learning fundamentals, with the first providing a longer 100-day commitment and broader community adoption, while the second offers a more condensed 50-day theoretical-practical balance.
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 50-Days-of-ML
prakhar21/50-Days-of-ML
A day to day plan for this challenge (50 Days of Machine Learning) . Covers both theoretical and practical aspects
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