# udacity/machine-learning

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4,027 stars · 6,239 forks · Jupyter Notebook · archived

## Links

- GitHub: https://github.com/udacity/machine-learning
- awesome-repositories: https://awesome-repositories.com/repository/udacity-machine-learning.md

## Description

This project is a machine learning curriculum and data science educational resource. It provides a structured set of instructional materials and hands-on projects designed for learning machine learning concepts and the implementation of predictive models.

The resource functions as a training guide for supervised learning, focusing on the development of models for image classification and digit recognition. It uses a project-based training approach that pairs theoretical lessons with dataset-driven model training and evaluation.

The curriculum covers the mathematical foundations of machine learning, data processing, and the implementation of supervised learning algorithms. It organizes content into modular units and sequential paths that move from theoretical study to the practical application of models using real-world datasets.

## Tags

### Education & Learning Resources

- [Machine Learning Education](https://awesome-repositories.com/f/education-learning-resources/educational-resources/systems-applied-computing/machine-learning-education.md) — Provides a comprehensive instructional resource for teaching the concepts, algorithms, and implementation of machine learning. ([source](https://github.com/udacity/machine-learning/blob/master/README.md))
- [Structured Curricula](https://awesome-repositories.com/f/education-learning-resources/supervised-learning-examples/structured-curricula.md) — Offers a structured machine learning curriculum focused on supervised learning algorithms for classification and digit recognition.
- [Pedagogical Frameworks](https://awesome-repositories.com/f/education-learning-resources/curricula-instructional-design/educational-frameworks-architectures/instructional-methodologies/pedagogical-frameworks.md) — Implements a pedagogical workflow that transitions from mathematical foundations to practical model evaluation.
- [Sequential Learning Paths](https://awesome-repositories.com/f/education-learning-resources/curriculum-guides/sequential-learning-paths.md) — Organizes material into a linear path of theoretical lessons and practical projects.
- [Curriculum Structures](https://awesome-repositories.com/f/education-learning-resources/curriculum-structures.md) — Provides a structured sequential path of concepts and projects to guide learners through the material.
- [Machine Learning Curricula](https://awesome-repositories.com/f/education-learning-resources/curriculum-structures/machine-learning-curricula.md) — Provides a structured learning path specifically designed for mastering machine learning concepts and algorithms.
- [Data Science Curricula](https://awesome-repositories.com/f/education-learning-resources/data-science-curricula.md) — Serves as an educational resource providing learning paths for data science and predictive modeling.
- [Model Training Guides](https://awesome-repositories.com/f/education-learning-resources/model-training-guides.md) — Provides a comprehensive guide and step-by-step tutorials for training image classification and digit recognition models.
- [Project-Based Learning](https://awesome-repositories.com/f/education-learning-resources/project-based-learning.md) — Uses the construction of functional machine learning models as the primary vehicle for teaching and validation.
- [Modular Learning Units](https://awesome-repositories.com/f/education-learning-resources/curricula-instructional-design/curricula-roadmaps/foundations-study-skills/pedagogical-support-study-resources/modular-learning-units.md) — Organizes complex technical subjects into independent, self-contained instructional blocks for easier study.
- [Supervised Learning Tutorials](https://awesome-repositories.com/f/education-learning-resources/technical-domain-education/ai-machine-learning-education/machine-learning-fundamentals/linear-regression-tutorials/supervised-learning-tutorials.md) — Provides guided tutorials on implementing various supervised learning models to analyze patterns in data.
- [Technical Learning Paths](https://awesome-repositories.com/f/education-learning-resources/technical-learning-paths.md) — Connects theoretical content with external datasets and code templates to facilitate technical training.

### Artificial Intelligence & ML

- [Data Science Training Programs](https://awesome-repositories.com/f/artificial-intelligence-ml/end-to-end-training-pipelines/data-science-training-programs.md) — Offers an end-to-end educational path for mastering data analysis and predictive modeling.
- [Machine Learning Education](https://awesome-repositories.com/f/artificial-intelligence-ml/machine-learning-education.md) — Focuses on teaching the mathematical and theoretical foundations of machine learning through a structured curriculum.
- [Learning Paths](https://awesome-repositories.com/f/artificial-intelligence-ml/machine-learning-foundations/learning-paths.md) — Organizes the curriculum into structured sequences that pair theoretical foundations with practical data-driven training.
- [Project-Based Model Assessments](https://awesome-repositories.com/f/artificial-intelligence-ml/machine-learning/infrastructure/model-evaluation-and-validation/model-capability-assessment/project-based-model-assessments.md) — Validates conceptual understanding by requiring model implementation on real-world datasets for specific classification tasks.
- [Machine Learning Concepts](https://awesome-repositories.com/f/artificial-intelligence-ml/machine-learning/machine-learning-concepts.md) — Teaches the fundamental mathematical and structural principles that define how machine learning models function. ([source](https://github.com/udacity/machine-learning#readme))
- [Pedagogical Exercises](https://awesome-repositories.com/f/artificial-intelligence-ml/model-training/dataset-driven-training/pedagogical-exercises.md) — Implements a project-based approach pairing theoretical lessons with hands-on, dataset-driven model training exercises.
- [Predictive Model Implementations](https://awesome-repositories.com/f/artificial-intelligence-ml/numerical-computing-libraries/algorithm-implementations/predictive-model-implementations.md) — Provides coded examples and exercises for implementing predictive models for classification and recognition tasks. ([source](https://github.com/udacity/machine-learning/tree/master/projects))
- [Predictive Model Development](https://awesome-repositories.com/f/artificial-intelligence-ml/predictive-model-development.md) — Guides the process of designing, training, and testing models for image classification and digit recognition.
- [Dataset-to-Lesson Mappings](https://awesome-repositories.com/f/artificial-intelligence-ml/machine-learning/machine-learning-concepts/ai-conceptual-research/dynamic-mind-mapping-tools/knowledge-mapping/dataset-to-lesson-mappings.md) — Links conceptual educational lessons directly to specific datasets for immediate practical application in model training.

### Software Engineering & Architecture

- [Project-Based Mastery Validations](https://awesome-repositories.com/f/software-engineering-architecture/schema-based-state-validation/model-based-state-validation/base-model-validations/project-based-mastery-validations.md) — Confirms student mastery by requiring the implementation of a functioning model on a real-world dataset.

### Part of an Awesome List

- [Modular Course Architectures](https://awesome-repositories.com/f/awesome-lists/learning/machine-learning-courses/modular-course-architectures.md) — Separates learning objectives into independent units focused on distinct machine learning techniques.
- [Learning and Reference](https://awesome-repositories.com/f/awesome-lists/ai/learning-and-reference.md) — Listed in the “Learning and Reference” section of the TopDeepLearning awesome list.