AI TRAINING
Feature Engineering Fundamentals for ML
Transform raw data into high-quality features that meaningfully improve machine learning model performance.
What it covers
This practitioner-level training teaches analysts and data professionals how to systematically engineer features from structured and semi-structured data. Participants learn categorical encoding strategies, numerical scaling, interaction and polynomial features, temporal feature extraction, and how to prevent target leakage. The programme combines hands-on labs in Python (pandas, scikit-learn) with real datasets, and closes with an introduction to feature stores for production pipelines. Participants leave with a reusable feature engineering playbook they can apply to their own datasets immediately.
What you'll be able to do
- Apply at least five categorical encoding strategies and justify which to use for a given dataset and model type
- Build temporal features including lag variables, rolling aggregates, and cyclical encodings from raw datetime columns
- Detect and eliminate target leakage in a feature pipeline using validation-set chronological splitting
- Implement a reusable feature transformation pipeline using scikit-learn Pipeline and ColumnTransformer
- Register and retrieve features from a basic feature store setup using Feast or Hopsworks
Topics covered
- Categorical encoding: ordinal, one-hot, target, and frequency encoding
- Numerical scaling: min-max, standardisation, robust scaling, log transforms
- Interaction features and polynomial feature construction
- Temporal and date-based feature extraction (lag, rolling windows, seasonality)
- Handling missing values as features vs. imputation strategies
- Target leakage detection and prevention techniques
- Feature selection methods: filter, wrapper, and embedded approaches
- Introduction to feature stores (Feast, Hopsworks) for production reuse
Delivery
Delivered over two to three days either in-person or live-virtual (Zoom/Teams). Roughly 40% concept instruction and 60% hands-on lab work. Each module pairs a short lecture with a Jupyter notebook exercise on a real-world dataset (e-commerce or financial). Participants receive a GitHub repo with all materials, a feature engineering checklist, and a reusable sklearn pipeline template. Remote delivery requires participants to have Python 3.10+ and a configured conda environment (setup guide provided in advance).
What makes it work
- Anchoring every exercise to a real business dataset the participants recognise, increasing relevance and retention
- Introducing feature stores early so participants see how engineered features are reused in production rather than recreated per model
- Pairing feature engineering training with a model evaluation module so participants can measure the impact of each transformation
- Encouraging participants to bring their own dataset for a capstone exercise during the final session
Common mistakes
- Encoding the target variable before splitting data, causing leakage that inflates validation scores
- Applying scaling or encoding fit on the full dataset rather than only on training folds
- Creating dozens of interaction features without a selection step, leading to the curse of dimensionality
- Treating feature engineering as a one-off step rather than building reproducible, versioned transformation pipelines
When NOT to take this
This training is not the right fit for teams that have not yet established a baseline ML workflow, if participants have never trained and evaluated a model end-to-end, a broader ML fundamentals course should come first.
Example providers
This page describes a training topic. It gives no price or duration: those depend on the provider and your team. The free diagnostic tells you which trainings your team needs first.