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Course Outline

Introduction to Machine Learning in Finance

  • The role of AI and ML within the financial industry
  • Different approaches to machine learning (supervised, unsupervised, reinforcement)
  • Real-world examples in fraud detection, credit scoring, and risk modeling

Python and Data Management Essentials

  • Leveraging Python for data processing and analysis
  • Analyzing financial data using Pandas and NumPy
  • Visualizing data with Matplotlib and Seaborn

Supervised Learning for Financial Forecasting

  • Linear and logistic regression techniques
  • Decision trees and random forests
  • Assessing model performance metrics (accuracy, precision, recall, AUC)

Unsupervised Learning and Anomaly Identification

  • Clustering methods (K-means, DBSCAN)
  • Principal Component Analysis (PCA)
  • Detecting outliers to prevent fraud

Credit Scoring and Risk Assessment

  • Creating credit scoring models with logistic regression and tree-based methods
  • Managing imbalanced datasets in risk scenarios
  • Ensuring model interpretability and fairness in financial decisions

Fraud Detection via Machine Learning

  • Identifying common forms of financial fraud
  • Applying classification algorithms to detect anomalies
  • Strategies for real-time scoring and model deployment

Model Deployment and AI Ethics in Finance

  • Deploying models using Python, Flask, or cloud services
  • Addressing ethical concerns and regulatory standards (e.g., GDPR, explainability)
  • Monitoring and retraining models in production settings

Conclusion and Future Directions

Requirements

  • Fundamental knowledge of statistics and financial principles
  • Familiarity with Excel or similar data analysis tools
  • Basic programming skills, ideally in Python

Target Audience

  • Financial analysts
  • Actuaries
  • Risk officers
 21 Hours

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