Build practical skills in AI and machine learning for investing and learn how financial data, alternative data, and intelligent models can support stronger investment decisions. Designed for finance and investment professionals, this course explores big-data architecture, machine-learning algorithms, financial prediction, model optimization, risk management, and data-driven trading applications.

You’ll apply feature engineering, validation, performance metrics, NLP, embeddings, topic modeling, sentiment analysis, and explainability to financial reports, earnings calls, market news, social-media signals, and alternative data. You’ll also examine transformers, large language models (LLMs), prompt-based workflows, and generative AI for financial research, reporting, document analysis, and investment intelligence. As you progress, you’ll evaluate model performance, market and sector signals, infrastructure requirements, privacy, bias, ethics, governance, regulatory compliance, and human oversight. By the end, you’ll be able to evaluate AI-enabled investment workflows, identify practical investment opportunities, interpret financial and sentiment signals, and apply responsible AI principles to investment research and decision-making.