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Live Discussion On Handling Imbalanced Dataset- Machine Learning
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Classroom Contents
Feature Engineering
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- 1 Feature Engineering-How to Perform One Hot Encoding for Multi Categorical Variables
- 2 Different Types of Feature Engineering Encoding Techniques
- 3 Why Do We Need to Perform Feature Scaling?
- 4 How To Handle Missing Values in Categorical Features
- 5 Featuring Engineering- Handle Categorical Features Many Categories(Count/Frequency Encoding)
- 6 Featuring Engineering- How To Handle Ordinal Categories(Ordinal Encoding)
- 7 Live-Feature Engineering-All Techniques To Handle Missing Values- Day 1
- 8 Live-Feature Engineering-All Techniques To Handle Missing Values- Day 2
- 9 Live-Feature Engineering-All Techniques To Handle Missing Values- Day 3
- 10 Live-Feature Engineering-All Techniques To Handle Categorical Features - Day 4
- 11 Summary Live Streaming-Feature Engineering- Probability Ratio Encoding- Handling Categorical Feature
- 12 Live-Feature Engineering-All Standardization And Transformation Techniques- Day 6
- 13 Live Discussion On Handling Imbalanced Dataset- Machine Learning
- 14 Live Discussion On Outlier And Its Impacts On Machine Learning UseCases
- 15 Discussing All The Types Of Feature Transformation In Machine Learning
- 16 Step By Step Process In EDA And Feature Engineering In Data Science Projects
