100 Days Of ML Code

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.
Learning and Reference - 100-day ML coding challenge (Chinese).

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MLEveryday100-Days-Of-ML-Code

Features

Open-source alternatives to 100 Days Of ML Code

Dod-o/Statistical-Learning-Method_Code

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Star history

Open-source alternatives to 100 Days Of ML Code

Dod-o/Statistical-Learning-Method_Code

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