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Course Outline

Machine Learning Foundations in Finance

  • Overview of AI and ML applications in the financial industry
  • Categories of machine learning (supervised, unsupervised, reinforcement learning)
  • Case studies covering fraud detection, credit scoring, and risk modeling

Python Essentials and Data Management

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

Supervised Learning for Financial Forecasting

  • Linear and logistic regression models
  • Decision trees and random forests
  • Assessing model performance (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

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

Fraud Detection via Machine Learning

  • Common types of financial fraud
  • Applying classification algorithms for anomaly detection
  • Strategies for real-time scoring and deployment

Model Deployment and AI Ethics in Finance

  • Deploying models using Python, Flask, or cloud platforms
  • Ethical considerations and regulatory compliance (e.g., GDPR, explainability)
  • Monitoring and retraining models in production settings

Recap and Future Directions

Requirements

  • Familiarity with fundamental statistics and financial principles
  • Proficiency with Excel or alternative data analysis tools
  • Foundational programming skills, ideally in Python

Target Audience

  • Financial analysts
  • Actuaries
  • Risk officers
 21 Hours

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