Training Your First ML Model

Let's walk through the typical workflow for building a machine learning model.

1. Define the problem

What are you predicting, and how will success be measured? Clarity here saves time later.

2. Collect and explore data

Gather relevant data and explore it. Look for missing values, outliers, and patterns.

3. Prepare the data

Clean, transform, and split into training, validation, and test sets. Good data prep often matters more than model choice.

4. Choose and train a model

Start simple (e.g. linear or tree-based models). Train on the training set and check the validation set.

5. Evaluate

Measure performance with appropriate metrics. Watch for overfitting.

6. Iterate and deploy

Tune features and settings, then deploy and monitor in the real world.

Key takeaways

• Start with a clear problem and clean data.
• Begin with simple models before going complex.
• ML is an iterative loop, not a one-shot task.