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Building a successful machine learning system requires more than just training a model. It involves a structured workflow from problem definition to deployment, along with best practices that prevent common mistakes and ensure reliable, reproducible results. This lesson brings everything together into a complete ML workflow.
| Step | Description | Key Activities |
|---|---|---|
| 1 | Define the Problem | Understand the business question, define success metrics |
| 2 | Collect Data | Gather relevant data from databases, APIs, files |
| 3 | Explore Data (EDA) | Visualise distributions, correlations, missing values |
| 4 | Preprocess Data | Clean, impute, scale, encode features |
| 5 | Engineer Features | Create new features, select important ones |
| 6 | Train Models | Try multiple algorithms, use cross-validation |
| 7 | Evaluate Models | Compare metrics, analyse errors |
| 8 | Tune Hyperparameters | Grid search, randomised search |
| 9 | Deploy Model | Serve predictions in production |
| 10 | Monitor and Maintain | Track performance, retrain when needed |
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