9798316916764 - Machine Learning for Corporate Finance Decision Making di Van Der Post, Hayden; Publishing, Reactive; Bisette, Vincent (9 risultati)

Condizione: As New. Unread book in perfect condition.

Condizione: New.

Condizione: New. In.

Condizione: New.

Condizione: As New. Unread book in perfect condition.

Paperback. Condizione: new. Paperback. Reactive PublishingMachine Learning for Corporate Finance Decision MakingHarnessing AI to Transform Strategic Financial ManagementIn an era defined by data, volatility, and exponential technological growth, corporate finance can no longer rely solely on intuition and static models. Machine Learning for Corporate Finance Decision Making is a comprehensive guide designed for modern finance professionals seeking to harness the power of artificial intelligence to gain a competitive edge.Written with clarity and depth, this book bridges the gap between theoretical ML concepts and their real-world applications in financial strategy. From capital budgeting and credit risk modeling to forecasting, cost optimization, and algorithmic decision systems, each chapter delivers actionable insights supported by practical Python code, business use cases, and implementation strategies.Whether you're a CFO, financial analyst, data scientist in finance, or MBA student looking to stay ahead of the curve, this book equips you with the tools to: Integrate supervised and unsupervised learning into your financial workflowsBuild dynamic forecasting models for revenue, cash flow, and market behaviorUse clustering, decision trees, and regression for cost management and valuationApply NLP to automate financial reporting and investor communicationsUnderstand the ethical and regulatory considerations in AI-driven financeThis is more than a textbook-it's a strategic manual for the next generation of financial leadership. Embrace the future of finance, where decisions are not only informed-but intelligent. This item is printed on demand. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.

PAP. Condizione: New. New Book. Shipped from UK. THIS BOOK IS PRINTED ON DEMAND. Established seller since 2000.

PAP. Condizione: New. New Book. Delivered from our UK warehouse in 4 to 14 business days. THIS BOOK IS PRINTED ON DEMAND. Established seller since 2000.

Paperback. Condizione: new. Paperback. Reactive PublishingMachine Learning for Corporate Finance Decision MakingHarnessing AI to Transform Strategic Financial ManagementIn an era defined by data, volatility, and exponential technological growth, corporate finance can no longer rely solely on intuition and static models. Machine Learning for Corporate Finance Decision Making is a comprehensive guide designed for modern finance professionals seeking to harness the power of artificial intelligence to gain a competitive edge.Written with clarity and depth, this book bridges the gap between theoretical ML concepts and their real-world applications in financial strategy. From capital budgeting and credit risk modeling to forecasting, cost optimization, and algorithmic decision systems, each chapter delivers actionable insights supported by practical Python code, business use cases, and implementation strategies.Whether you're a CFO, financial analyst, data scientist in finance, or MBA student looking to stay ahead of the curve, this book equips you with the tools to: Integrate supervised and unsupervised learning into your financial workflowsBuild dynamic forecasting models for revenue, cash flow, and market behaviorUse clustering, decision trees, and regression for cost management and valuationApply NLP to automate financial reporting and investor communicationsUnderstand the ethical and regulatory considerations in AI-driven financeThis is more than a textbook-it's a strategic manual for the next generation of financial leadership. Embrace the future of finance, where decisions are not only informed-but intelligent. This item is printed on demand. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability.