Avik-Jain/100-Days-Of-ML-Code
100 Days Of ML Code
This project is a structured educational curriculum designed to guide developers through the fundamentals of machine learning. It functions as a technical skill builder, offering a curated roadmap of progressive coding challenges that cover core algorithms, statistical concepts, and essential data science libraries.
The repository distinguishes itself through an iterative sequencing of content, organizing complex technical topics into a daily progression that facilitates incremental mastery. It integrates third-party academic lectures and educational resources to provide necessary theoretical context, which is then paired with library-centric implementations that translate mathematical theory into functional code.
The curriculum encompasses a broad capability surface, including deep learning foundations, statistical model implementation, and data science essentials. Learners engage with these topics through modular units that utilize interactive computational documents, allowing for the combination of live code, mathematical explanations, and visual data exploration to verify model performance.
Features
- Machine Learning Implementations - A comprehensive repository of implementation examples covering core algorithms, statistical concepts, and essential data science libraries.
- Machine Learning Tutorials - Provides a comprehensive collection of machine learning tutorials and algorithm implementations.
- Programming Roadmaps - A curated roadmap of progressive programming challenges focused on mastering data manipulation, mathematical foundations, and neural network architectures.
- Machine Learning Frameworks - Relies on standard data science ecosystems to abstract complex mathematical operations into functional interfaces.
- Machine Learning Curricula - A structured collection of daily coding exercises and study notes designed to guide beginners through machine learning fundamentals.
- Deep Learning Frameworks - Provides tutorials for deep learning using standard industry frameworks.
- Data Science Curricula - Guides learners through essential data science libraries like NumPy, Pandas, and Matplotlib.
- Learning Paths - Organizes complex technical concepts into a structured daily progression for incremental skill acquisition.
- Machine Learning Study Paths - Structured learning paths for developers to master core algorithms and data science techniques through daily hands-on coding practice.
- Awesome List - A community-curated directory that catalogs and links out to other open-source projects, rather than a standalone tool you run yourself.
- Deep Learning Tutorials - Provides tutorials on analyzing neural network models using visualization tools.
- Neural Network Architectures - Provides tutorials on convolutional neural networks and their implementation.
- Learning and Reference - 100-day ML coding challenge.
- Machine Learning - Structured learning path for mastering machine learning over 100 days.
- Machine Learning Models - Comprehensive machine learning tutorial and code repository.
- Interview Preparation - A 100-day challenge to learn machine learning through coding.
- Algorithm Implementation Exercises - Guides the implementation of regression, classification, and clustering algorithms from the ground up.
- Deep Learning Courses - Provides foundational learning materials for neural networks and deep learning frameworks.
- Data Science Tooling Tutorials - Provides a deep dive into Matplotlib for data visualization.
- Educational Modules - Encapsulates specific algorithms or mathematical theories into isolated, self-contained modules for focused study.
- Introductory Machine Learning - Explains the fundamental concepts of what a neural network is.
- Mathematical Foundations Courses - Provides training on the linear algebra and calculus concepts underlying machine learning models.
- Backpropagation Theory - Explains the mechanics and purpose of the backpropagation algorithm.
- Curricula - Integrates theoretical academic content with practical code implementations to provide context.
- Mathematical Foundations - Explains the calculus behind backpropagation in neural networks.
- Optimization Theory - Explains gradient descent and how neural networks learn.
- Numerical Libraries - Provides deep-dive tutorials on using numerical computing libraries.
Star history
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