Data Pre Processing
Data Pre Processing
Feature Engineering
Feature Engineering
less than a minute
This course includes
5 articles
9 mins reading time
5 videos • 53 mins
Course content
5 sections • 29 topics • Read: 9 mins • Video: 53 mins
Data Pre Processing
Real World Data
Missing Values
Handle Missing Values (Imputation)
Handle Outliers
Scaling and Normalization
Data Pre Processing in Machine Learning | Missing Values | Outliers | Scaling | Normalization
Categorical Variables
Categorical Variables
One Hot Encoding (OHE)
Label ️ Encoding
Ordinal Encoding
Frequency/Count Encoding
Target Encoding
Hash Encoding
Encoding Categorical Variables | Label | Ordinal | Target | Count | Hash | One Hot Encoding
Feature Engineering
Feature Engineering
Polynomial Features
Feature Crossing
Hash Encoding
Binning (Discretization)
Feature Engineering | Polynomial Features | Feature Crossing | Hash Encoding | Binning | Explained
Data Leakage
Data Leakage
Train-Test Contamination
Temporal Leakage
Target Leakage
Data Leakage in ML | Target Leakage | Temporal Leakage | Train Test Contamination | Explained
Model Interpretability
House Price Prediction
Can we explain why the model made a certain prediction ?
Feature Importance
Why Model Interpretability Matters ?
Trust
Model Debuggability
Feature Engineering
Regulatory Compliance
Model Interpretability in ML | Why Model Interpretability is Important ?
Categorical Variables
Categorical Variables
Feature Engineering
Feature Engineering
Data Leakage
Data Leakage
Model Interpretability
Model Interpretability