Cingillioglu ilker (6 Ergebnisse)

Zustand: very good. Gut/Very good: Buch bzw. Schutzumschlag mit wenigen Gebrauchsspuren an Einband, Schutzumschlag oder Seiten. / Describes a book or dust jacket that does show some signs of wear on either the binding, dust jacket or pages.

PAP. Zustand: New. New Book. Shipped from UK. Established seller since 2000.

Zustand: As New.

PAP. Zustand: New. New Book. Shipped from UK. Established seller since 2000.

Paperback. Zustand: Brand New. 318 pages. 6.00x0.86x9.00 inches. In Stock.

Taschenbuch. Zustand: Neu. Druck auf Anfrage Neuware - Printed after ordering - Every organisation now has access to powerful AI tools. Very few know how to use them to make better decisions.This book is about the gap between having AI and using it well. It is written for analysts, managers, clinicians, lawyers, engineers, policy professionals, and researchers - anyone whose work involves consequential decisions and who wants to make those decisions more rigorously, more consistently, and more accountably in an age of machine intelligence.Drawing on decision science, cognitive psychology, applied statistics, and ten chapters of worked Python examples, Dr. Ilker Cingillioglu builds a complete framework for AI-augmented decision-making - from the anatomy of a decision and the cognitive biases that corrupt it, to the design of decision pipelines, the governance of automated systems, and the professional skills that remain irreducibly human when machines handle the analysis.Inside this book:Why AI is a decision technology, not just a productivity tool - and what that distinction means for competitive advantageThe anatomy of a decision: structured, semi-structured, and unstructured problems, and where AI helps and where it doesn'tHow to use AI as a genuine reasoning partner - frameworks for Socratic dialogue, pre-mortem analysis, and structured co-thinkingThe four-layer decision stack, from data ingestion to action, with documented failure modes at each layerPython in practice: pandas, NumPy, scikit-learn, and matplotlib applied to real decision workflows, with full code examplesA systematic algorithm comparison exercise - Naive Bayes, Random Forest, SVM, and Gradient Boosting - applied to a customer churn datasetDecision-making across seven domains: business strategy, healthcare, education, finance, public policy, engineering, and lawHow to build AI-augmented workflows that actually reach decision-makers - from automated pipelines to decision engines with feedback loopsThe ethics, accountability, and governance requirements that separate AI deployment that works from AI deployment that harmsThe future professional: the analyst-builder-strategist hybrid who will define the next decade of knowledge workWho this book is forThis book was written for the professional who is serious about AI but not necessarily technical - the manager who needs to govern AI systems they did not build, the analyst who wants to build decision models without a computer science degree, the researcher who wants to use AI as a genuine methodological partner rather than a shortcut. No prior programming experience is required. A willingness to think precisely is. …