Все публикации

0:04:48

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

0:01:54

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

0:04:16

Cost-sensitive learning in scikit-learn

0:07:31

How to read the scikit-learn documentation

2:47:31

My top 50 scikit-learn tips

0:24:40

21 more pandas tricks

0:07:49

Adapt this pattern to solve many Machine Learning problems

0:05:07

Tune multiple models simultaneously with GridSearchCV

0:03:38

Access part of a Pipeline using slicing

0:04:07

Tune the parameters of a VotingClassifer or VotingRegressor

0:04:32

Ensemble multiple models using VotingClassifer or VotingRegressor

0:04:08

Create feature interactions using PolynomialFeatures

0:02:16

Speed up GridSearchCV using parallel processing

0:06:59

Use OrdinalEncoder instead of OneHotEncoder with tree-based models

0:03:11

Passthrough some columns and drop others in a ColumnTransformer

0:04:07

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

0:01:36

Estimators only print parameters that have been changed

0:02:11

Load a toy dataset into a DataFrame

0:02:18

Get the feature names output by a ColumnTransformer

0:02:08

Create an interactive diagram of a Pipeline in Jupyter

0:01:56

Most parameters should be passed as keyword arguments

0:01:23

Don't use .values when passing a pandas object to scikit-learn

0:02:29

Add feature selection to a Pipeline

0:04:09

Use FunctionTransformer to convert functions into transformers