Feature Engineering Training

Introduction to Feature Engineering

Understand the basics of feature engineering and its importance in the machine learning workflow. Learn how feature engineering enhances model performance and contributes to more accurate predictions.

Understanding Features and Their Impact

Explore the different types of features and their impact on machine learning models. Learn about numerical, categorical, and textual features, and how they affect model training and outcomes.

Data Preprocessing

Study techniques for data preprocessing, including handling missing values, scaling and normalizing data, and encoding categorical variables. Learn how to prepare raw data for feature extraction.

Feature Extraction and Transformation

Learn methods for extracting and transforming features from raw data. Explore techniques such as dimensionality reduction, feature selection, and generating new features from existing data.

Feature Engineering for Numerical Data

Explore feature engineering techniques specifically for numerical data. Learn how to create meaningful features through aggregation, binning, and interaction terms to improve model performance.

Feature Engineering for Categorical Data

Study methods for engineering features from categorical data. Learn about encoding techniques such as one-hot encoding, label encoding, and target encoding, and their impact on model training.

Feature Engineering for Textual Data

Understand how to engineer features from textual data. Learn about text preprocessing, tokenization, and vectorization methods such as TF-IDF and word embeddings for improving text-based models.

Feature Selection and Dimensionality Reduction

Study techniques for feature selection and dimensionality reduction. Learn how to use methods like Principal Component Analysis (PCA), Recursive Feature Elimination (RFE), and feature importance metrics.

Feature Engineering for Time Series Data

Explore feature engineering techniques for time series data. Learn about creating time-based features, handling seasonality, and designing features that capture temporal patterns and trends.

Case Studies and Practical Exercises

Engage with case studies and practical exercises to apply feature engineering concepts. Work on real-world projects to develop hands-on skills in transforming data and optimizing machine learning models.