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Data Science with Python: AI Applications for Finance
Reactive Publishing
In a world where algorithms dominate markets and data drives decisions, Data Science with AI Applications for Finance equips you with the tools and techniques to lead the charge. Whether you're an aspiring quant, a financial analyst, or a seasoned professional looking to integrate cutting-edge AI into your workflow, this comprehensive guide delivers the essential knowledge you need to thrive in modern finance.
Inside, you'll
Core Python foundations tailored for financial modeling and analytics
Step-by-step implementations of machine learning and deep learning in trading, risk management, and portfolio optimization
Real-world case studies applying AI to fraud detection, robo-advisory systems, and algorithmic trading
Time series forecasting using LSTM, ARIMA, and Prophet
How to harness NLP for sentiment analysis on market data and news feeds
Deployment strategies for scalable AI models in production environments
Each chapter blends theoretical insights with practical, hands-on Python code examples, ensuring you not only understand the concepts—but can build and deploy them immediately.
Whether you're building automated trading systems, enhancing financial decision-making, or preparing for the future of fintech, this book is your roadmap to mastering the intersection of AI, Python, and finance.
In a world where algorithms dominate markets and data drives decisions, Data Science with AI Applications for Finance equips you with the tools and techniques to lead the charge. Whether you're an aspiring quant, a financial analyst, or a seasoned professional looking to integrate cutting-edge AI into your workflow, this comprehensive guide delivers the essential knowledge you need to thrive in modern finance.
Inside, you'll
Core Python foundations tailored for financial modeling and analytics
Step-by-step implementations of machine learning and deep learning in trading, risk management, and portfolio optimization
Real-world case studies applying AI to fraud detection, robo-advisory systems, and algorithmic trading
Time series forecasting using LSTM, ARIMA, and Prophet
How to harness NLP for sentiment analysis on market data and news feeds
Deployment strategies for scalable AI models in production environments
Each chapter blends theoretical insights with practical, hands-on Python code examples, ensuring you not only understand the concepts—but can build and deploy them immediately.
Whether you're building automated trading systems, enhancing financial decision-making, or preparing for the future of fintech, this book is your roadmap to mastering the intersection of AI, Python, and finance.
725 pages, Kindle Edition
Published April 5, 2025
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