This comprehensive course specializes in data cleaning and preprocessing techniques in Python, preparing you to apply these techniques in predictive modeling. The course covers a range of relevant topics, from missing data handling to feature engineering.
Overview
Syllabus
- Lesson 1: Data Cleaning Techniques: Detecting and Handling Missing Data
- Lesson 2: Data Cleaning Techniques: Working with Categorical Data Encoding and Transformation
- Lesson 3: Diving into Data Transformation and Scaling Techniques
- Lesson 4: Navigating through Outliers: Detection and Handling Techniques
- Lesson 5: Understanding and Handling Redundant or Correlated Features in Datasets
- Lesson 6: Engineering New Features for Better Predictions
