NESA Software Engineering - Machine Learning OOP Implementation Examples

This Jupyter Notebook collection is designed to support students implement Programming for automation in the NESA Software Engineering Syllabus specifically using an OOP to make predictions. Open these Jupyter Notebooks in Jupyter Notebook, VSCode or Codespaces to modify the code/data and run the code blocks.

Important

The configuration for VSCode and Codespaces have been built into this repository. To ensure the necessary decencies are installed and configured it is recommended that a GitHub Codespace is used for the Advanced Neural Network and Decision Tree implementations.

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Demonstrations

1. OOP Linear Regression Implementation.
2. OOP Multiple Feature Linear Regression Implementation.
3. OOP Polynomial Linear Regression Implementation.
4. OOP Logistic Regression Classification Implementation.
5. OPP K-Nearest Neighbour Classification Implementation
6. OOP Neural Network Linear Regression Implementation.
7. OOP Neural Network Image Classification Implementation.
8. OOP Decision Trees Image Classification Implementation.

Other Jupyter Notebooks In the TempeHS Machine Learning Series

1. Scikit-learn Linear Regression, A Jupyter Notebook collection designed to support students' understanding of the Linear Regression model defined in the NESA Software Engineering Course Specifications pg 28.
2. NESA Software Engineering - Machine Learning OOP Implementation Examples, A Jupyter Notebook collection designed to support students implement Programming for automation in the NESA Software Engineering Syllabus specifically using an OOP to make predictions.
3. Practical-Application-of-NESA-Software-Engineering-MLOps, A Jupyter Notebook collection designed to develop a practical understanding of Machine Learning Operations (MLOps) defined in the NESA Software Engineering Course Specifications pg 27.

Machine Learning OOP Implementation Examples by Ben Jones is licensed under Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International