Machine Learning with Python: From Theory to Fraud Detection in Uganda’s Mobile Money Ecosystem

Описание: Overview
This video presentation serves as the final assessment for the Alison Diploma in Machine Learning with Python. It is divided into two parts: a comprehensive review of the core Machine Learning modules and a practical application of these theories to solve Mobile Money Fraud in Uganda.

Part 1: Course Curriculum Summary (Slides 1–9)
I break down the fundamental concepts mastered during the diploma, including:

Introduction to ML: Frameworks for training, testing, and the Python data ecosystem.

K-Nearest Neighbors (KNN): Understanding proximity-based classification and its limitations in high-dimensional data.

Decision Trees: Mapping logical decision paths and handling non-linear relationships.

Ensemble Learning & Random Forests: How to utilize "Bagging", "Boosting", and multiple decision trees to ensure model stability and prevent Overfitting.

Support Vector Machines (SVM): Utilizing hyperplanes for optimal class separation.

Principal Component Analysis (PCA): Applying dimensionality reduction to simplify complex datasets while preserving critical information.

Part 2: Mobile Money Fraud Detection (Slides 10–12)
Applying these modules to a local challenge: Financial Fraud in Uganda.

The Problem: Identifying "Non-Linear" fraud patterns that bypass static security rules.

The Solution: A Multimodal ML Engine using Geo-Intelligence and NLP to protect student tuition and merchant transactions.

The Outcome: Distinguishing between "Abnormal but Valid" behavior and real-time theft using Probabilistic Risk Scoring.