course | Data Preprocessing and Feature Engineering Masterclass (Structured & Unstructured Data)

Foundations of Data Preprocessing 

• Cleaning structured and semi-structured data 
• Handling missing values, outliers, and inconsistencies 
• Encoding categorical data and scaling numeric features 
• Introduction to text preprocessing 

Feature Selection and Engineering Techniques 

• Filter, wrapper, and embedded feature selection 
• Creating interaction and derived features 
• Handling multicollinearity and feature importance analysis 
• Combining text and numeric features in predictive models 

Dimensionality Reduction for Complex Data 

• PCA, Truncated SVD, t-SNE, and UMAP 
• Dimensionality reduction for sparse and dense matrices 
• Reducing noise while preserving informative features 
• Visualizing high-dimensional data after reduction 

Advanced NLP and Text Feature Engineering 

• Tokenization, stemming, lemmatization, and vectorization 
• Word embeddings and semantic representation 
• Reducing feature space for text analytics 
• Text preprocessing for sentiment, classification, and clustering 

Capstone: Integrated Data Pipeline 

• Building end-to-end preprocessing pipelines 
• Combining structured and unstructured data processing 
• Feature engineering and dimensionality reduction for analytics 
• Preparing clean, high-quality data for dashboards, ML, or analytics