Data Preprocessing and Feature Engineering

36 lessons in AI Fundamentals

• Coalesce Won't Increase Spark PartitionsCode Quiz
• Distributed Feature Scaling and Shuffles in SparkQuiz
• Imputation Leakage and Downstream BiasCode Quiz
• Imputation Leakage and Downstream BiasQuiz
• Shuffles and Joins in Distributed Feature EngineeringFlashcard
• Imputation Bias and Missingness MechanismsFlashcard
• Partitioning, Shuffles, and Joins in SparkSlides / Video
• Imputation Bias in Missing DataSlides / Video
• Imputing Sentinel-Coded Missing ValuesCode Quiz
• Missing Data Mechanisms and ImputationQuiz
• Scaling Before the Train/Test SplitCode Quiz
• Preventing Leakage in Scaled Feature PipelinesQuiz
• Feature Pipelines at ScaleFlashcard
• Missing Data, Outliers & Data QualityFlashcard
• Feature Pipelines at ScaleSlides / Video
• Missing Data, Outliers & Data QualitySlides / Video
• Mean Imputation With Sentinel ValuesCode Quiz
• Preventing Data Leakage in ImputationQuiz
• Scaling Train and Test DataCode Quiz
• Encoding High-Cardinality Categorical FeaturesQuiz
• Feature Engineering & Encoding EssentialsFlashcard
• Feature Engineering & Encoding EssentialsSlides / Video
• Dropping Missing Rows in PandasCode Quiz
• Handling Missing Data in PandasQuiz
• Filtering a DataFrame with two conditionsCode Quiz
• Filtering Rows in PandasQuiz
• Handling Missing Data & DuplicatesSlides / Video
• Pandas: Load, Filter & Select DataSlides / Video
• Handling Missing Data & DuplicatesFlashcard
• Pandas: Loading, Filtering & Selecting DataFlashcard
• Encoding, Scaling & TransformationsSlides / Video
• Handling Missing Data & OutliersSlides / Video
• Encoding, Scaling & TransformationsFlashcard
• Handling Missing Data & OutliersFlashcard
• Detecting Outliers with IQRCode Quiz
• One-Hot Encoding a Category ColumnCode Quiz