100 Days Of ML Code

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.

Features

Machine Learning Education - Provides a comprehensive instructional resource for teaching the fundamental concepts, algorithms, and implementations of machine learning.

Supervised Learning Models - Implements predictive models such as linear regression and decision trees to map inputs to known target outputs.

Unsupervised Learning Algorithms - Implements clustering algorithms like K-Means to identify hidden patterns and structures within unlabeled datasets.

Clustering and Density Estimation - Implements unsupervised clustering techniques such as K-Means and Hierarchical clustering to discover patterns in unlabeled data.

Algorithm Implementations - Provides pedagogical code references and theoretical explanations for standard machine learning algorithms.

Machine Learning Courses - Provides a structured training program covering the theory and implementation of linear regression, decision trees, and SVMs.

Learning Path Guides - Provides a structured sequence of daily milestones and modules to guide learners through a complete machine learning curriculum.

Unsupervised Learning - Delivers educational content on algorithms that identify patterns in unlabeled data, including K-Means and hierarchical clustering.

Interactive Notebook Environments - Delivers instructional content through interactive notebooks that interleave theoretical explanations with executable code.

Machine Learning Curricula - Offers a structured 100-day educational program and syllabus for mastering machine learning fundamentals.

Data Preprocessing - Includes tools and tutorials for cleaning, normalizing, and transforming datasets before model training.

Mathematics for Machine Learning - Provides a collection of mathematical foundations including linear algebra and calculus for understanding model functions.

Data Science Tutorials - Includes guides for preprocessing raw data and building predictive models using common Python libraries.

Linear Algebra Resources - Includes learning materials covering linear algebra, calculus, and optimization techniques necessary for machine learning.

Mathematical Foundations Study Guides - Breaks down complex mathematical foundations into isolated thematic sections covering linear algebra, calculus, and optimization.

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Features

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