kaieye/2022-Machine-Learning-Specialization
2022 Machine Learning Specialization
This repository is a collection of machine learning course materials, providing study notes and Python implementation examples for a professional specialization. It serves as a guide for supervised and unsupervised learning, focusing on the application of fundamental algorithms.
The content covers a broad range of machine learning education, including the mathematical foundations and practical prototyping of models. It specifically provides resources for implementing regression, classification, clustering, and dimensionality reduction techniques.
The project is organized as a curriculum-based hierarchy using Jupyter Notebooks for executable code and Markdown files for theoretical notes. It includes a modular exercise structure that separates algorithm implementations from validation scripts.
Features
- Machine Learning Education - Provides a comprehensive curriculum focused on teaching the fundamental concepts and implementation of machine learning models.
- Machine Learning Implementations - Includes code-based reference implementations of fundamental supervised and unsupervised machine learning algorithms.
- Study Notes - Provides annotated summaries and theoretical key takeaways as study aids for the curriculum.
- Machine Learning Courses - Provides a structured training program teaching the theory and practical application of machine learning models.
- Jupyter Notebook Curricula - Provides structured learning paths delivered as interactive Jupyter notebooks with embedded exercises.
- Educational ML Algorithm Libraries - Provides a collection of machine learning models implemented from scratch for the purpose of study and reference.
- Unsupervised Learning - Provides educational content on unsupervised learning algorithms used to identify patterns in unlabeled data.
- Machine Learning Algorithm Study Guides - Provides comprehensive study guides exploring supervised and unsupervised learning methods including regression and clustering.
- Supervised Learning Tutorials - Includes educational guides on implementing various supervised learning models such as regression and classification.
- Modular Exercise Structuring - Separates algorithm implementations from validation scripts to ensure student-written code is correct.
