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Data Preprocessing Part 5: Handling Imbalanced Data
A complete guide to handling imbalanced datasets in machine learning—covering SMOTE, class weighting, ensemble methods, and metrics like PR-AUC and F1-score. Learn practical strategies to build reliable models on skewed class distributions across real-world use cases.
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Data Preprocessing Part 4: Feature Engineering
A comprehensive guide to feature engineering in machine learning—covering feature selection methods, interaction terms, time-based features, text transformations like TF-IDF and BERT, and discretization techniques. Learn to build smarter models with domain-driven, pipeline-ready features.
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Data Preprocessing Part 3: Data Transformation in Practice
A practical guide to data transformation for machine learning—covering scaling, normalization, categorical encoding, date parsing, and preprocessing for text and images. Learn to build robust pipelines, prevent data leakage, and prepare high-quality inputs for modeling.
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Data Preprocessing Part 2: Mastering the Mess with Effective Data Cleaning
A detailed guide to data cleaning for machine learning—covering structural validation, missing value imputation, outlier detection, text normalization, and categorical consolidation. Learn how to transform messy data into reliable, model-ready inputs with practical strategies and real-world techniques.
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Data Preprocessing Part 1: Exploring, Profiling, and Collecting Data the Right Way
A practical guide to data collection, profiling, and exploratory data analysis (EDA) across formats like text, images, time-series, and geospatial data. Learn how to assess quality, detect bias, handle missingness, and apply domain-aware diagnostics before modeling.