02. ML Fundamentals
Train, Validation, Test split
Opening Hook The Problem with Two Sets Most beginners think they need only two...
Cross-Validation
Cross Validation in Machine Learning is a technique used to evaluate machine learning models...
Overfitting vs Underfitting
Overfitting vs Underfitting In machine learning there are the two most common hurdles every...
Bias-Variance Tradeoff
Bias Variance Tradeoff Is one of the most important concepts in machine learning. Every...
Feature Scaling
“Imagine comparing a person’s height in centimeters with their weight in kilograms. The numbers...
Encoding Categorical Variables
Encoding Categorical Variables Is an essential step in machine learning because models only understand...
Handling Imbalanced Datasets
Handling Imbalanced Datasets in Machine Learning Is one of the most common problems in...
Feature Engineering for ML
Feature Engineering for ML Are the most important part of building high-performing models. Your...
Feature Selection
Feature Selection in Machine Learning “More features does not mean better model. Extra features...
Scikit-learn Pipeline
Master Scikit-learn Pipeline “You clean data. Then you scale it. Then you encode categories....