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course content

Зміст курсу

ML Introduction with scikit-learn

1. Machine Learning Concepts

What is MLTypes of Machine LearningTraining SetTypes of DataMachine Learning Workflow

2. Preprocessing Data with Scikit-learn

Scikit-learn ConceptsGetting Familiar with DatasetDealing with Missing ValuesImpute Missing ValuesOrdinalEncoderOne-Hot EncoderLabelEncoderEncode Categorical VariablesWhy Scale the Data?StandardScaler, MinMaxScaler, MaxAbsScalerScale the FeaturesPreprocessing Summary

3. Pipelines

What is PipelineColumnTransformerPipelineCreating a Pipeline ChallengeFinal EstimatorCreate a Complete ML Pipeline

4. Modeling

ModelsKNeighborsClassifierEvaluating a Model. Train-Test split.Cross-ValidationEvaluate the Model with Cross-ValidationGridSearchCVThe Flaw of GridSearchCVTune Hyperparameters with RandomizedSearchCVModeling SummaryPutting It All Together

ML Introduction with scikit-learn

Preprocessing SummaryPreprocessing Summary

That's it for the preprocessing. The three problems we addressed were missing values, categorical values, and unscaled data.
These are the most frequent problems datasets face, so Imputation, Encoding, and Scaling are included in almost every pipeline.
Soon you will learn how to make pipelines in sklearn, making it easy to put everything together.
Now let's revise what transformer we learned:

Imputers (Dealing with missing values)

ImputerWhat for
SimpleImputer(strategy='most_frequent')Impute categorical data
SimpleImputer(strategy='mean'/'median')Impute numerical data

Encoders (Dealing with categorical values)

EncoderWhat for
OrdinalEncoderEncode ordinal features
OneHotEncoderEncode nominal features
LabelEncoderEncode target

Scalers (Dealing with different scales)

ScalerWhat for
MinMaxScalerScale the features to a [0,1] range
MaxAbsScalerScale the features to a [-1,1] range
StandardScalerScale the features so that the mean is 0 and the variance is 1

Все було зрозуміло?

Секція 2. Розділ 12
course content

Зміст курсу

ML Introduction with scikit-learn

1. Machine Learning Concepts

What is MLTypes of Machine LearningTraining SetTypes of DataMachine Learning Workflow

2. Preprocessing Data with Scikit-learn

Scikit-learn ConceptsGetting Familiar with DatasetDealing with Missing ValuesImpute Missing ValuesOrdinalEncoderOne-Hot EncoderLabelEncoderEncode Categorical VariablesWhy Scale the Data?StandardScaler, MinMaxScaler, MaxAbsScalerScale the FeaturesPreprocessing Summary

3. Pipelines

What is PipelineColumnTransformerPipelineCreating a Pipeline ChallengeFinal EstimatorCreate a Complete ML Pipeline

4. Modeling

ModelsKNeighborsClassifierEvaluating a Model. Train-Test split.Cross-ValidationEvaluate the Model with Cross-ValidationGridSearchCVThe Flaw of GridSearchCVTune Hyperparameters with RandomizedSearchCVModeling SummaryPutting It All Together

ML Introduction with scikit-learn

Preprocessing SummaryPreprocessing Summary

That's it for the preprocessing. The three problems we addressed were missing values, categorical values, and unscaled data.
These are the most frequent problems datasets face, so Imputation, Encoding, and Scaling are included in almost every pipeline.
Soon you will learn how to make pipelines in sklearn, making it easy to put everything together.
Now let's revise what transformer we learned:

Imputers (Dealing with missing values)

ImputerWhat for
SimpleImputer(strategy='most_frequent')Impute categorical data
SimpleImputer(strategy='mean'/'median')Impute numerical data

Encoders (Dealing with categorical values)

EncoderWhat for
OrdinalEncoderEncode ordinal features
OneHotEncoderEncode nominal features
LabelEncoderEncode target

Scalers (Dealing with different scales)

ScalerWhat for
MinMaxScalerScale the features to a [0,1] range
MaxAbsScalerScale the features to a [-1,1] range
StandardScalerScale the features so that the mean is 0 and the variance is 1

Все було зрозуміло?

Секція 2. Розділ 12
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