ML Practice: End-to-End Project Workflow

🎯 Learning Objectives

• Execute a complete ML project from problem definition to deployment
• Apply data cleaning, EDA, and feature engineering to real datasets
• Select, tune, and evaluate models systematically
• Understand deployment considerations

📖 Core Content

1.1 The ML Project Lifecycle

1.2 Problem Definition Framework

1.3 Project Checklist

1. Define: Frame the ML problem, set success criteria, identify stakeholders
2. Explore: Profile data, check distributions, identify issues
3. Clean: Handle missing values, outliers, inconsistencies
4. Engineer: Create features, encode categories, scale
5. Model: Start simple (linear), iterate to complex
6. Tune: Cross-validate, hyperparameter search
7. Evaluate: Test set, business metrics, error analysis
8. Deploy: API, monitoring, CI/CD pipeline

📝 Practice Questions

Q1: Why start with a simple model (linear regression, logistic regression)?

1. Baseline: Know if complex models are worth it
2. Debugging: Simpler to verify data pipeline
3. Fast iteration: Quick feedback on data quality
4. Interpretability: Stakeholders understand linear models
5. Production: Simple model may be good enough (cheaper, faster) Q2: What's the difference between a business metric and an ML metric?

Business metric: revenue increased, costs reduced, customer retention improved. ML metric: accuracy, F1, RMSE. They must be aligned — improving ML metrics should improve business metrics. Sometimes they conflict (higher accuracy doesn't always mean higher revenue). Join Discord NextData Cleaning