Applied analytics learning press (7 résultats)

Etat : New.

Paperback. Etat : new. Paperback. This book gives professionals, students, educators, managers, analysts, entrepreneurs, and curious readers a clear map of modern AI in applied, easy-to-follow language. It explains how the major pieces fit together, including machine learning, deep learning, generative AI, prompt engineering, retrieval-augmented generation (RAG), generative adversarial networks (GANs), AI agents, human-centered design, model evaluation, risk, and business value.Rather than treating AI as a single technology, this guide shows AI as a landscape of connected ideas, tools, decisions, and tradeoffs. You will learn how models are trained, how prompts shape outputs, how retrieval can ground answers in documents, how agents use tools and workflows, how synthetic media is created, and how to evaluate whether an AI system is accurate, useful, safe, and worth the cost.Inside, you will learn how to think about: - Machine learning, deep learning, RAG, GANs, and generative AI- Prompts, tokens, delimiters, retrieval, guardrails, and AI agents- Human oversight, explainability, privacy, bias, and risk- Accuracy, false positives, false negatives, and model evaluation- Data quality, workflows, cost, ROI, and business value- Human-centered AI design and practical product decisions- When to use AI-and when a simpler solution is betterThe book uses diagrams, tables, examples, checklists, code-oriented illustrations, and field-guide explanations to make complex AI concepts easier to compare, remember, and apply. It is designed to help readers build a working mental model, not just memorize definitions.Use this guide if you are evaluating AI tools, teaching AI basics, planning an AI initiative, supporting technical teams, designing an AI-powered product, or exploring an entrepreneurial AI idea. It will help you ask better questions, recognize common failure modes, understand tradeoffs, and communicate more clearly with both technical and nontechnical audiences. A practical field guide to machine learning, generative AI, RAG, AI agents, human-centered design, risk, evaluation, and business value. This item is printed on demand. Shipping may be from multiple locations in the US or from the UK, depending on stock availability. …

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

PAP. Etat : 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. Etat : new. Paperback. This book gives professionals, students, educators, managers, analysts, entrepreneurs, and curious readers a clear map of modern AI in applied, easy-to-follow language. It explains how the major pieces fit together, including machine learning, deep learning, generative AI, prompt engineering, retrieval-augmented generation (RAG), generative adversarial networks (GANs), AI agents, human-centered design, model evaluation, risk, and business value.Rather than treating AI as a single technology, this guide shows AI as a landscape of connected ideas, tools, decisions, and tradeoffs. You will learn how models are trained, how prompts shape outputs, how retrieval can ground answers in documents, how agents use tools and workflows, how synthetic media is created, and how to evaluate whether an AI system is accurate, useful, safe, and worth the cost.Inside, you will learn how to think about: - Machine learning, deep learning, RAG, GANs, and generative AI- Prompts, tokens, delimiters, retrieval, guardrails, and AI agents- Human oversight, explainability, privacy, bias, and risk- Accuracy, false positives, false negatives, and model evaluation- Data quality, workflows, cost, ROI, and business value- Human-centered AI design and practical product decisions- When to use AI-and when a simpler solution is betterThe book uses diagrams, tables, examples, checklists, code-oriented illustrations, and field-guide explanations to make complex AI concepts easier to compare, remember, and apply. It is designed to help readers build a working mental model, not just memorize definitions.Use this guide if you are evaluating AI tools, teaching AI basics, planning an AI initiative, supporting technical teams, designing an AI-powered product, or exploring an entrepreneurial AI idea. It will help you ask better questions, recognize common failure modes, understand tradeoffs, and communicate more clearly with both technical and nontechnical audiences. A practical field guide to machine learning, generative AI, RAG, AI agents, human-centered design, risk, evaluation, and business value. This item is printed on demand. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability. …

Paperback. Etat : new. Paperback. This book gives professionals, students, educators, managers, analysts, entrepreneurs, and curious readers a clear map of modern AI in applied, easy-to-follow language. It explains how the major pieces fit together, including machine learning, deep learning, generative AI, prompt engineering, retrieval-augmented generation (RAG), generative adversarial networks (GANs), AI agents, human-centered design, model evaluation, risk, and business value.Rather than treating AI as a single technology, this guide shows AI as a landscape of connected ideas, tools, decisions, and tradeoffs. You will learn how models are trained, how prompts shape outputs, how retrieval can ground answers in documents, how agents use tools and workflows, how synthetic media is created, and how to evaluate whether an AI system is accurate, useful, safe, and worth the cost.Inside, you will learn how to think about: - Machine learning, deep learning, RAG, GANs, and generative AI- Prompts, tokens, delimiters, retrieval, guardrails, and AI agents- Human oversight, explainability, privacy, bias, and risk- Accuracy, false positives, false negatives, and model evaluation- Data quality, workflows, cost, ROI, and business value- Human-centered AI design and practical product decisions- When to use AI-and when a simpler solution is betterThe book uses diagrams, tables, examples, checklists, code-oriented illustrations, and field-guide explanations to make complex AI concepts easier to compare, remember, and apply. It is designed to help readers build a working mental model, not just memorize definitions.Use this guide if you are evaluating AI tools, teaching AI basics, planning an AI initiative, supporting technical teams, designing an AI-powered product, or exploring an entrepreneurial AI idea. It will help you ask better questions, recognize common failure modes, understand tradeoffs, and communicate more clearly with both technical and nontechnical audiences. A practical field guide to machine learning, generative AI, RAG, AI agents, human-centered design, risk, evaluation, and business value. This item is printed on demand. Shipping may be from our Sydney, NSW warehouse or from our UK or US warehouse, depending on stock availability. …

Taschenbuch. Etat : Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - This book gives professionals, students, educators, managers, analysts, entrepreneurs, and curious readers a clear map of modern AI in applied, easy-to-follow language. It explains how the major pieces fit together, including machine learning, deep learning, generative AI, prompt engineering, retrieval-augmented generation (RAG), generative adversarial networks (GANs), AI agents, human-centered design, model evaluation, risk, and business value.Rather than treating AI as a single technology, this guide shows AI as a landscape of connected ideas, tools, decisions, and tradeoffs. You will learn how models are trained, how prompts shape outputs, how retrieval can ground answers in documents, how agents use tools and workflows, how synthetic media is created, and how to evaluate whether an AI system is accurate, useful, safe, and worth the cost.Inside, you will learn how to think about:¿ Machine learning, deep learning, RAG, GANs, and generative AI¿ Prompts, tokens, delimiters, retrieval, guardrails, and AI agents¿ Human oversight, explainability, privacy, bias, and risk¿ Accuracy, false positives, false negatives, and model evaluation¿ Data quality, workflows, cost, ROI, and business value¿ Human-centered AI design and practical product decisions¿ When to use AI-and when a simpler solution is betterThe book uses diagrams, tables, examples, checklists, code-oriented illustrations, and field-guide explanations to make complex AI concepts easier to compare, remember, and apply. It is designed to help readers build a working mental model, not just memorize definitions.Use this guide if you are evaluating AI tools, teaching AI basics, planning an AI initiative, supporting technical teams, designing an AI-powered product, or exploring an entrepreneurial AI idea. It will help you ask better questions, recognize common failure modes, understand tradeoffs, and communicate more clearly with both technical and nontechnical audiences.…