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

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

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.