Feature engineering is the process of transforming raw data into features that can be used for machine learning models.

One-Hot Encoding, Normalization, and Dimensionality Reduction are all common techniques used for feature engineering.

One-hot encoding is used to convert categorical variables into numerical variables.

Normalization is used to scale the features in a dataset to a common range.

Dimensionality reduction is used to reduce the number of features in a dataset, improve the accuracy of a machine learning model, and make the data more interpretable.

Principal Component Analysis (PCA), Linear Discriminant Analysis (LDA), and t-SNE are all common dimensionality reduction techniques.

Feature selection is used to select the most relevant features for a machine learning model, reduce the number of features in a dataset, and improve the accuracy of a machine learning model.

Filter Methods, Wrapper Methods, and Embedded Methods are all common feature selection techniques.

A feature importance score is used to measure the importance of each feature in a machine learning model.

Permutation Importance, Gini Importance, and Information Gain are all common methods for calculating feature importance scores.

A feature engineering pipeline is used to automate the feature engineering process, make the feature engineering process more reproducible, and improve the accuracy of a machine learning model.

scikit-learn, pandas, and NumPy are all common feature engineering pipeline tools.

A feature engineering notebook is used to document the feature engineering process, share the feature engineering process with others, and make the feature engineering process more reproducible.

Jupyter Notebook, Google Colab, and Kaggle Notebooks are all common feature engineering notebook tools.

The best way to learn feature engineering is to read books and articles about feature engineering, take online courses about feature engineering, and practice feature engineering on real-world datasets.