100-Days-Of-ML-Code is a machine learning curriculum and instructional resource designed as a structured 100-day learning path. It provides a sequence of daily milestones that cover the mathematical foundations and practical implementations of machine learning algorithms.
The project is organized into specialized courses for supervised and unsupervised learning. Supervised learning materials cover the implementation of predictive models such as linear regression, decision trees, and support vector machines. Unsupervised learning materials focus on clustering models, including K-Means and hierarchical clustering, to identify patterns in unlabeled data.
The curriculum includes study guides for theoretical foundations in linear algebra, calculus, and optimization. It also provides tutorials for data science workflows, specifically focusing on data preprocessing and the creation of visualizations to prepare raw datasets for modeling.
Instructional content is delivered through interactive notebooks that combine theoretical explanations with live code implementations.
