pandas best practices (10/10): Fixing bad data

Course outline: "Master Machine Learning with scikit-learn"

Course overview: "Master Machine Learning with scikit-learn"

Introduction to model ensembling

How to save a scikit-learn Pipeline with custom transformers

Should I shuffle samples with cross-validation?

Cost-sensitive learning in scikit-learn

scikit-learn vs Deep Learning

How to read the scikit-learn documentation

My top 50 scikit-learn tips

21 more pandas tricks

Adapt this pattern to solve many Machine Learning problems

Tune multiple models simultaneously with GridSearchCV

Access part of a Pipeline using slicing

Tune the parameters of a VotingClassifer or VotingRegressor

Ensemble multiple models using VotingClassifer or VotingRegressor

Create feature interactions using PolynomialFeatures

Speed up GridSearchCV using parallel processing

Use OrdinalEncoder instead of OneHotEncoder with tree-based models

Passthrough some columns and drop others in a ColumnTransformer

Drop the first category from binary features (only) with OneHotEncoder

Estimators only print parameters that have been changed

Load a toy dataset into a DataFrame

Get the feature names output by a ColumnTransformer

Create an interactive diagram of a Pipeline in Jupyter