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Kickoff: What Are AI, Machine Learning, and Predictive Analytics?

Welcome!

Let’s Get on the Same Page: Simple Definitions and Real-world Examples

Where they overlap and where they don’t: concepts, methods, and outcomes

Digging Deeper: How the Pieces Fit Together

Data is the fuel: features, labels, and feedback loops

From rules to learning: comparing rule-based AI, ML models, and predictive pipelines

Measuring success: accuracy, precision, recall, and business impact

Hands-On Thinking: Applying the Ideas

Case walk-through: predicting student success vs. building a smart tutor

Ethics and limits: bias, drift, and responsible use

Check Your Understanding: Quizzes

Quiz 1: Core definitions and comparisons

Quiz 2: Overlap vs. differences in practice

Quiz 3: Data, features, and feedback loops

Quiz 4: Metrics and outcomes that matter

Quiz 5: Cases, ethics, and responsible choices

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References for this course

AI, Machine Learning, and Predictive Analytics: Clear Distinctions is a practical, jargon-light course designed to finally demystify these often-confused terms. In under a week, you’ll learn what each discipline is, how they overlap, and—crucially—where they diverge in goals, tools, workflows, and outcomes. Through concise explanations, real-world examples, and side-by-side comparisons, you’ll connect concepts like rule-based AI, supervised learning, and predictive pipelines to concrete business and product decisions. You’ll also build intuition for data essentials—features, labels, training signals—and how feedback loops shape system behavior over time. The course goes beyond definitions to show how to measure success with metrics that matter: accuracy, precision, recall, and business impact. You’ll walk through a hands-on case that contrasts predicting student success with building a smart tutor, so you can see how design choices shift depending on the problem. Finally, you’ll examine responsible use—bias, drift, ethics—and how to make better choices in the real world. Ideal for product managers, analysts, leaders, and curious technologists, this course gives you the clarity and vocabulary to communicate with stakeholders, scope projects effectively, and choose the right approach for your next data or AI initiative.

AI, Machine Learning, and Predictive Analytics: Clear Distinctions

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            <p>AI, Machine Learning, and Predictive Analytics get mixed up a lot, but they’re not the same thing. This course breaks them apart so you can see what each does, where they work together, and why that matters in the real world. We start by building a shared vocabulary—simple definitions, clear examples, and side-by-side comparisons—so you can tell when something is rule-based AI, when it’s a model learning from data, and when it’s a predictive system making a forecast. You’ll see relatable scenarios, like streaming recommendations, school performance predictions, and chatbots, to ground the ideas.</p>

<p>Once you’ve got the basics, we dig into how these systems work under the hood. You’ll learn why data is the “fuel,” what features and labels are, and how feedback loops can improve (or hurt) performance. We’ll compare rule-based systems to ML models and full predictive pipelines, then practice reading results using metrics like accuracy, precision, recall, and—most importantly—what those numbers mean for real outcomes. Finally, you’ll apply your understanding with a case walk-through (predicting student success vs. building a smart tutor) and tackle the big questions—bias, model drift, and responsible use. Quizzes throughout help you check your understanding, and you’ll leave with the confidence to describe, compare, and evaluate AI, ML, and predictive analytics like a pro.</p>
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