Section outline

  • Lesson Goal: Summarize and reinforce the end-to-end process of solving problems with machine learning – from problem definition and data gathering to model deployment and maintenance – giving students a roadmap to follow for their own projects and an appreciation of how all the pieces (from previous lessons) fit into a coherent workflow. This ties together technical skills with project management and strategy, essential for “mastery.”

    • ·         Micro-topic 25.1: Defining the Problem and Success Criteria (Goal: Emphasize that a clear understanding of the problem and what success looks like is the crucial first step in any ML project)

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      • Micro-topic 25.2: Data Collection and Preparation – The “80% of the Work” (Goal: Convey that gathering the right data and preparing it (cleaning, labeling, splitting, features) is often the most time-consuming but critical part of ML; teach best practices in data prep)
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      • Micro-topic 25.3: Model Selection and Training – Picking the Right Tool for the Job (Goal: Discuss how to choose a suitable model (simple vs complex, interpretable vs black box, etc.), and cover training best practices like using validation sets, avoiding overfitting, and iterating)
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      • Micro-topic 25.4: Model Evaluation and Iteration – Refining Your Solution (Goal: Cover how to properly evaluate a model (beyond just overall accuracy; considering confusion matrix, precision/recall, etc. where appropriate), testing on real conditions, and iterating by analyzing errors. Emphasize continuous improvement.)
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      • Micro-topic 25.5: Deployment and Maintenance – From Model to Production (Goal: Discuss what happens after modeling: deploying the model into a real environment, considerations like speed, scalability, and the need for ongoing monitoring and updating. Emphasize ML is not done until it’s delivering value in the real world.)
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      • Micro-topic 25.6: The Big Picture – Continuous Learning and Improvement (Goal: Conclude the course by putting the process in perspective: encourage a mindset of continuous learning – both the model continuously learning from new data and the practitioner learning from each project; tie back to “winning against AI” theme by stressing that mastering the process is an ongoing journey where adaptability is key)
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