Machine Learning¶

Welcome to the M9 Machine Learning module microsite for the BSc (Hons) Data Science programme at BPP University School of Technology.

About This Site¶

This site follows the Diátaxis framework for technical documentation, organising content into four types:

Type	Purpose	When to Use
Tutorials	Learning-oriented step-by-step exercises	When learning a new technique
How-to Guides	Task-oriented solutions to specific problems	When solving a workplace problem
Reference	Information-oriented lookup tables and API docs	When you need exact parameters or syntax
Explanation	Understanding-oriented conceptual discussions	When you need to understand why

The ML Pipeline¶

graph LR
    A[Business Question] --> B[Data Preparation]
    B --> C[Feature Engineering]
    C --> D[Model Selection]
    D --> E[Training & Tuning]
    E --> F[Validation]
    F --> G[Communication & Impact]
    G -.-> A

Topics Overview¶

#	Topic	Assessment Section
1	Data Preparation	Section A — Methodology
2	Feature Engineering	Section A — Methodology
3	Predictive Modelling	Section A — Methodology
4	Non-Parametric Modelling	Section A — Methodology
5	Clustering	Section A — Methodology
6	Time Series	Section A — Methodology
7	Validation & Tuning	Section B — Results
8	Communication & Impact	Section C — Impact

Getting Started¶

New to machine learning? Start with Environment Setup, then work through the topics in order.

Apprenticeship Context

Throughout this site, examples use real-world business scenarios relevant to your workplace. Concepts are connected to the Knowledge, Skills, and Behaviours (KSBs) in the L6 Data Science Apprenticeship Standard.

Interactive Learning¶

All code examples are designed to run in Jupyter Notebooks or Google Colab. Each tutorial includes complete, runnable Python code using scikit-learn, pandas, and other standard libraries.

Contributing¶

Found an error or have a suggestion? Please open an issue on GitHub.