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Course Outline

Introduction to Machine Learning in Finance

  • The role of AI and ML in the financial industry
  • Categories of machine learning (supervised, unsupervised, reinforcement learning)
  • Case studies focusing on fraud detection, credit scoring, and risk modeling

Python Fundamentals 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 Forecasts

  • Linear and logistic regression methods
  • Decision trees and random forest algorithms
  • Assessing model performance via accuracy, precision, recall, and AUC

Unsupervised Learning and Anomaly Identification

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

Credit Scoring and Risk Modeling

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

Machine Learning for Fraud Detection

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

Model Deployment and Ethics in Financial AI

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

Recap and Future Directions

Requirements

  • Familiarity with basic statistics and core financial concepts
  • Proficiency with Excel or other data analysis tools
  • Foundational programming knowledge, ideally in Python

Target Audience

  • Financial analysts
  • Actuaries
  • Risk officers
 21 Hours

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