Artificial intelligence and machine learning have become essential in extracting insights from data across industries — from healthcare and finance to marketing and supply chain. Yet for many practitioners and analysts, taking the leap from theory to real-world application can feel daunting, especially when faced with the complexity of coding and data engineering.
Demystifying AI: Data Science and Machine Learning Using IBM SPSS Modeler offers a practical, business-oriented path into AI and machine learning using IBM SPSS Modeler, a powerful visual analytics platform. Instead of starting with code, this book emphasizes workflow, interpretation, and impact — enabling you to tackle real data problems and build predictive models without writing a single line of code.
It’s part of the Data Mining and Knowledge Discovery Series, blending foundational concepts with usable techniques and practical examples — ideal for professionals who want to apply AI in business contexts with clarity and confidence.
Why This Book Matters
Many data science resources dive deeply into statistics, programming, or mathematical proofs — which can be intimidating if your goal is to solve business problems. This book takes a different approach: it focuses on applied data science, showing you how to use SPSS Modeler to explore data, build models, evaluate results, and interpret outcomes for decisions.
IBM SPSS Modeler’s visual, drag-and-drop environment makes it accessible for analysts and business users, while the book’s step-by-step guidance ensures that you understand why each method works and how it can inform real choices.
In short, it demystifies not just the tools, but the process of turning data into actionable insight.
What You’ll Learn
This book guides you through a complete data science lifecycle — from understanding data to deploying models — with clear explanations and SPSS Modeler examples.
1. Introduction to AI and Machine Learning Concepts
Rather than beginning with code, the book starts by helping you understand:
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What artificial intelligence really means
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The role of machine learning within AI
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Key concepts like supervised vs. unsupervised learning
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The importance of data quality and preparation
This foundational context sets you up to make sound modeling decisions.
2. Getting Started with IBM SPSS Modeler
Before building models, you’ll master the tool itself:
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Navigating the SPSS Modeler interface
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Importing and preparing data from multiple sources
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Understanding the data visualization and exploration tools
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Building pipelines visually with nodes and streams
This makes the analytics environment approachable and practical.
3. Data Preparation and Feature Engineering
Good models begin with good data. You’ll learn how to:
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Handle missing values and outliers
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Transform variables for better model performance
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Generate derived features
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Understand and reshape data structures
These steps help ensure that your models learn from signal rather than noise.
4. Building Predictive Models
Once data is ready, the book shows how to build and evaluate machine learning models using SPSS Modeler’s visual tools:
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Classification models (decision trees, logistic regression)
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Regression for continuous outcomes
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Clustering and segmentation (e.g., k-means)
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Association and pattern discovery
Each technique is explained in terms of business relevance and model interpretation, not just algorithm mechanics.
5. Evaluating and Interpreting Models
The book emphasizes how to assess models responsibly:
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Cross-validation and hold-out testing
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Confusion matrices and performance metrics
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ROC curves, precision, recall, and accuracy
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Interpreting coefficients and decision paths
This helps you choose models that work in practice, not just in theory.
6. Applying Models to Business Problems
What distinguishes this book is its focus on practical use cases. You’ll see how to:
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Predict customer churn
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Segment customers for targeted marketing
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Forecast demand or outcomes
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Evaluate risk in finance or operations
By grounding lessons in business scenarios, the book helps you translate data into decision-ready insight.
Who This Book Is For
This book is ideal for:
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Business analysts who want to add predictive modeling to their skill set
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Data professionals transitioning from Excel or BI tools to machine learning
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Operations and strategy leaders wanting to understand how AI is applied
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Students building practical analytics competencies
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Anyone who wants to use machine learning without becoming a coder
You don’t need deep statistical or programming background — the book builds from intuitive concepts to practical execution.
What Makes This Book Valuable
Visual, Tool-First Learning
It uses SPSS Modeler’s visual workflows so you can focus on what the model does, not how to code it.
Application-Driven
Rather than abstract examples, the book ties methods to real business decisions and common analytics tasks.
Balanced Theory and Practice
Readers learn both why methods work and how to use them effectively in SPSS Modeler.
Accessible to Non-Coders
For professionals without programming experience, this book opens doors to machine learning using a GUI-based platform.
How This Helps Your Career and Projects
After studying this book, you’ll be able to:
✔ Prepare and clean real datasets
✔ Build and compare predictive models visually
✔ Explain model results to stakeholders
✔ Apply analytics to business problems with confidence
✔ Integrate machine learning into business processes
These abilities are valuable in:
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Business Intelligence
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Marketing Analytics
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Operations and Supply Chain
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Financial Risk Modeling
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Customer Insights and Strategy
Being able to deliver machine learning insights in production contexts can elevate your role and impact across teams.
Hard Copy: Demystifying AI: Data Science and Machine Learning Using IBM SPSS Modeler (Chapman & Hall/CRC Data Mining and Knowledge Discovery Series)
Kindle: Demystifying AI: Data Science and Machine Learning Using IBM SPSS Modeler (Chapman & Hall/CRC Data Mining and Knowledge Discovery Series)
Conclusion
Demystifying AI: Data Science and Machine Learning Using IBM SPSS Modeler is a practical, approachable guide that makes AI and machine learning accessible to professionals who want impact without unnecessary complexity. By focusing on real data workflows, visual modeling, and business use cases, it helps you build models that deliver insight — not just predictions.
If your goal is to understand and apply AI in real business contexts, this book gives you a clear path from foundational concepts to actionable outcomes using a powerful yet accessible analytics tool.
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