Module #1 Introduction to Machine Learning in Finance Overview of the course, importance of machine learning in finance, and its applications
Module #2 Basics of Machine Learning Introduction to machine learning, types of machine learning, and key concepts
Module #3 Financial Data Analysis Introduction to financial data, data preprocessing, and visualization
Module #4 Supervised Learning in Finance Introduction to supervised learning, linear regression, and logistic regression in finance
Module #5 Unsupervised Learning in Finance Introduction to unsupervised learning, clustering, and dimensionality reduction in finance
Module #6 Time Series Analysis in Finance Introduction to time series analysis, ARIMA, and ETS in finance
Module #7 Deep Learning in Finance Introduction to deep learning, neural networks, and their applications in finance
Module #8 Neural Networks for Financial Prediction Building neural networks for financial prediction, including stock price prediction and credit risk assessment
Module #9 Natural Language Processing in Finance Introduction to NLP, text analysis, and sentiment analysis in finance
Module #10 Portfolio Optimization using Machine Learning Using machine learning for portfolio optimization, including risk management and performance metrics
Module #11 Credit Risk Modeling using Machine Learning Using machine learning for credit risk modeling, including credit scoring and probability of default
Module #12 Market Risk Modeling using Machine Learning Using machine learning for market risk modeling, including Value-at-Risk (VaR) and Expected Shortfall (ES)
Module #13 Operational Risk Modeling using Machine Learning Using machine learning for operational risk modeling, including loss detection and severity prediction
Module #14 Alternative Data in Finance Introduction to alternative data sources, including social media, web scraping, and IoT data
Module #15 Machine Learning for Fraud Detection Using machine learning for fraud detection, including anomaly detection and supervised learning
Module #16 Machine Learning for Trading Using machine learning for trading, including high-frequency trading and algorithmic trading
Module #17 Explainability and Transparency in Machine Learning Importance of explainability and transparency in machine learning, including model interpretability and LIME
Module #18 Ethical Considerations in Machine Learning Ethical considerations in machine learning, including bias, fairness, and accountability
Module #19 Case Studies in Machine Learning in Finance Real-world case studies of machine learning applications in finance, including success stories and challenges
Module #20 Machine Learning in Fintech Applications of machine learning in fintech, including payment systems and digital lending
Module #21 Machine Learning in Investment Banking Applications of machine learning in investment banking, including M&A and IPO analysis
Module #22 Machine Learning in Asset Management Applications of machine learning in asset management, including portfolio rebalancing and risk management
Module #23 Machine Learning in Risk Management Applications of machine learning in risk management, including credit risk, market risk, and operational risk
Module #24 Future of Machine Learning in Finance Emerging trends and future directions in machine learning in finance, including AI and blockchain
Module #25 Course Wrap-Up & Conclusion Planning next steps in Machine Learning in Finance career
