Internship← Artificial Intelligence & Machine Learning learning pathArtificial Intelligence & Machine Learning · Module 9 of 10
Building ML projects
Go from problem to working model.
📖 Key ideas
- Define the problem and success metric.
- Collect and explore data (EDA).
- Train baseline then improve with features and tuning.
- Use scikit-learn pipelines for repeatable workflows.
- Tune hyperparameters with GridSearchCV or Optuna.
💡 Real-life example: An ML project is farming: prepare soil (data), plant seeds (models), tend (tune) and harvest (deploy).
BaselineSimple first model
PipelineChained preprocessing and model
HyperparameterSetting chosen before training
GridSearchCVExhaustive hyperparameter search