Transform raw data into powerful features that unlock your model's full potential
Transform from data novice to feature engineering expert
Master data preprocessing, handle missing values, encode categorical variables, and create new features from existing data to dramatically improve model performance
Use feature scaling, polynomial features, binning, log transforms, and dimensionality reduction (PCA, t-SNE) to prepare data for optimal ML results
Apply feature selection methods, eliminate redundant features, and engineer domain-specific features that professional data scientists use in production systems
Feature engineering is what separates good models from great models
Data manipulation and feature creation
Feature preprocessing and transformers
Professional feature engineering library
Automated feature engineering
Advanced categorical encoding techniques
Numerical operations and array manipulation
8 comprehensive tutorials covering essential feature engineering techniques
Learn to handle missing values, detect and remove outliers, clean messy data, and prepare datasets for ML. Master imputation strategies and data quality assessment.
⏱️ 65 min Start Learning →Master one-hot encoding, label encoding, ordinal encoding, target encoding, and frequency encoding. Learn when to use each technique for optimal results.
⏱️ 70 min Start Learning →Understand standardization, min-max scaling, robust scaling, and normalization. Learn why scaling matters and which method to use for different algorithms.
⏱️ 60 min Start Learning →Create polynomial features, interaction features, date/time features, text features (TF-IDF, word embeddings), and domain-specific engineered features.
⏱️ 75 min Start Learning →Apply log transforms, Box-Cox transforms, binning/discretization, and power transforms. Handle skewed distributions and create better features.
⏱️ 65 min Start Learning →Master filter methods (correlation, chi-square), wrapper methods (RFE), embedded methods (LASSO, tree importance), and reduce feature dimensionality.
⏱️ 70 min Start Learning →Apply PCA, t-SNE, UMAP, LDA, and autoencoders. Visualize high-dimensional data, reduce computational cost, and eliminate multicollinearity.
⏱️ 80 min Start Learning →Use Featuretools for automated feature generation, build feature engineering pipelines with scikit-learn, and deploy production-ready feature stores.
⏱️ 85 min Start Learning →Apply feature engineering to real-world datasets and build portfolio projects
Engineer features from housing data: handle missing values, encode neighborhoods, create age features, scale prices, and build a regression model.
Handle imbalanced data, create transaction features, apply anomaly detection techniques, and select the most predictive features for fraud detection.
Build automated feature engineering pipeline, create time-based features, apply dimensionality reduction, and deploy a production-ready model.
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