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Courses, in 8 stages

  1. 01Programming for AI/ML9 courses
  2. 02Statistics and ML Algorithms11 courses
  3. 03MLOps12 courses
  4. 04Deep Learning6 courses
  5. 05Agentic AI10 courses
  6. 06Time Series Forecasting9 courses
  7. 07Industry DS Projects15 courses
  8. 08Special Topics13 courses
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  1. Courses
  2. PySpark for Data Science - III: Data Cleaning and Analysis Methods
  3. Introduction to Data PreProcessing

Data Preprocessing Essence

Video · 4 min · Lesson 1 of 22

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MLOps and DeploymentPySpark for Data Science - III: Data Cleaning and Analysis Methods

    1. Data Preprocessing EssenceFree4 min
    2. Download Resources1 min
    3. Automate Variable type Identification8 min
    4. Self Assessment - 11 min
    1. Outlier Treatment Approaches7 min
    2. How to Detect and Treat Outliers using IQR method?4 min
    3. How to detect and treat outliers using the z-score?3 min
    4. Identifying and Removing Duplicates3 min
    5. Self Assessment - 21 min
    6. Course Review1 min
    1. Missing Value Treatment approaches3 min
    2. Approach 1: Detect and drop the row with missing values6 min
    3. Approach 2: Check conditions and drop an entire Column2 min
    4. Approach 3: Impute the missing data appropriately2 min
    5. Self assessment - 32 min
    1. StringIndexer5 min
    2. OneHot Encoding6 min
    1. Feature Min-Max scaling4 min
    2. Feature Standardization1 min
    1. Variance Inflation Factor (VIF)6 min
    2. PCA (Principal Component Analysis)3 min
    3. Final Self Assessment2 min

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