This course provides a clear and structured introduction to how artificial intelligence is transforming modern financial markets. Learners explore the core AI models used in trading—such as machine learning, natural language processing, and reinforcement learning—while understanding how these tools enhance decision-making, forecasting, and portfolio management. The program also examines the data, technology, risk frameworks, and ethical considerations essential for building reliable AI-driven trading systems. Designed for traders, analysts, data scientists, and investors, this course equips participants with the foundational knowledge and practical insights needed to navigate and leverage AI in today’s fast-evolving financial landscape.

Growth

Skills & learning objectives

There are 9 modules in this class


  • Historical Evolution of AI in Financial SystemsUnlock this class to watch4m 54s
  • Core AI Methodologies for Financial ApplicationsUnlock this class to watch14m 46s

  • Mechanisms of Financial TradingUnlock this class to watch7m 6s
  • Investment Strategies and Portfolio TheoryUnlock this class to watch5m 17s
  • Market Structures and Financial InstrumentsUnlock this class to watch9m 11s
  • Risk-Return DynamicsUnlock this class to watch6m 11s

  • Supervised and Unsupervised Machine LearningUnlock this class to watch13m 43s
  • Natural Language Processing for Sentiment AnalysisUnlock this class to watch6m 25s
  • Reinforcement Learning for Trading OptimizationUnlock this class to watch4m 7s

  • Taxonomy of Financial DataUnlock this class to watch6m 20s
  • Data Preprocessing and Feature SelectionUnlock this class to watch3m 30s
  • Challenges in Real-Time Data UtilizationUnlock this class to watch3m 19s
  • Data Governance and ComplianceUnlock this class to watch1m 33s
  • Feature Engineering Case StudyUnlock this class to watch1m 22s

  • Programming Languages and LibrariesUnlock this class to watch5m 39s
  • Cloud Computing and DeploymentUnlock this class to watch3m 16s
  • Overview of AI Trading EcosystemUnlock this class to watch2m 40s

  • Algorithmic Trading Systems and Case StudyUnlock this class to watch6m 34s
  • Automated Portfolio Management and OptimizationUnlock this class to watch3m 34s
  • Performance Metrics and EvaluationUnlock this class to watch2m 43s
  • Backtesting and Simulation TechniquesUnlock this class to watch2m
  • AI Development Lifecycle in Trading SystemsUnlock this class to watch2m 26s

  • AI Applications in Risk Modeling and Case StudyUnlock this class to watch7m 24s
  • Ethical Implications of AI in TradingUnlock this class to watch3m 22s
  • Regulatory Frameworks for AI in FinanceUnlock this class to watch3m 34s

  • Case Study 1: AI-Powered Sentiment Analysis for Trading Tesla StockUnlock this class to watch2m 18s
  • Case Study 2: Reinforcement Learning for Crypto Arbitrage on BinanceUnlock this class to watch2m 5s
  • ClosingUnlock this class to watch1m 5s

Final exam

Assess your learning with a final exam covering all class topics.

This class is part of the following certifications

Issued by Capital Academy and the American University in Dubai (AUD)
Certified Portfolio & Fund Manager (CPFM)

Certified Portfolio & Fund Manager (CPFM)

63 ClassesUnlimited interactive exercises

Unlock Certificate
Issued by Capital Academy and the American University in Dubai (AUD)
Certificate in AI-Powered Financial Analysis (CAFA)

Certificate in AI-Powered Financial Analysis (CAFA)

56 ClassesUnlimited interactive exercises

Unlock Certificate
Issued by Capital Academy and the American University in Dubai (AUD)
AI & Algo Trading Certification (AIAT)

AI & Algo Trading Certification (AIAT)

54 ClassesUnlimited interactive exercises

Unlock Certificate

ClassFAQs

The course aims to introduce learners to the foundations and practical applications of AI in trading and investment, including machine learning, deep learning, NLP, reinforcement learning, and their role in financial decision-making.
It is designed for individuals seeking to understand how AI transforms trading systems, portfolio management, risk modelling, and financial analytics.
The module covers supervised and unsupervised learning, deep learning architectures, reinforcement learning, and transformer-based NLP models, along with their applications in forecasting, signal generation, and sentiment analysis.
The course explains data types, preprocessing, validation, feature engineering, and real-time data pipelines, emphasizing data governance, compliance, and model reliability.
The course discusses model risk, bias, transparency, regulatory expectations, stress-testing, volatility forecasting, and responsible AI governance in finance.

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