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ML Introduction with scikit-learn
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Conteúdo do Curso

ML Introduction with scikit-learn

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

bookScale the Features

In this challenge, you need to scale the features using StandardScaler. The data is a good old Penguins dataset (encoded and with no missing values).


              
12345

            
import pandas as pd

df = pd.read_csv('https://codefinity-content-media.s3.eu-west-1.amazonaws.com/a65bbc96-309e-4df9-a790-a1eb8c815a1c/penguins_imputed_encoded.csv')

print(df)
copy

Here is a little reminder of StandardScaler class.

Tarefa

  1. Import the StandardScaler class
  2. Initialize a StandardScaler object.
  3. Fit and transform the X using that object.

Switch to desktopMude para o desktop para praticar no mundo realContinue de onde você está usando uma das opções abaixo
Tudo estava claro?

Como podemos melhorá-lo?

Obrigado pelo seu feedback!

Seção 2. Capítulo 11
toggle bottom row

bookScale the Features

In this challenge, you need to scale the features using StandardScaler. The data is a good old Penguins dataset (encoded and with no missing values).


              
12345

            
import pandas as pd

df = pd.read_csv('https://codefinity-content-media.s3.eu-west-1.amazonaws.com/a65bbc96-309e-4df9-a790-a1eb8c815a1c/penguins_imputed_encoded.csv')

print(df)
copy

Here is a little reminder of StandardScaler class.

Tarefa

  1. Import the StandardScaler class
  2. Initialize a StandardScaler object.
  3. Fit and transform the X using that object.

Switch to desktopMude para o desktop para praticar no mundo realContinue de onde você está usando uma das opções abaixo
Tudo estava claro?

Como podemos melhorá-lo?

Obrigado pelo seu feedback!

Seção 2. Capítulo 11
toggle bottom row

bookScale the Features

In this challenge, you need to scale the features using StandardScaler. The data is a good old Penguins dataset (encoded and with no missing values).


              
12345

            
import pandas as pd

df = pd.read_csv('https://codefinity-content-media.s3.eu-west-1.amazonaws.com/a65bbc96-309e-4df9-a790-a1eb8c815a1c/penguins_imputed_encoded.csv')

print(df)
copy

Here is a little reminder of StandardScaler class.

Tarefa

  1. Import the StandardScaler class
  2. Initialize a StandardScaler object.
  3. Fit and transform the X using that object.

Switch to desktopMude para o desktop para praticar no mundo realContinue de onde você está usando uma das opções abaixo
Tudo estava claro?

Como podemos melhorá-lo?

Obrigado pelo seu feedback!

In this challenge, you need to scale the features using StandardScaler. The data is a good old Penguins dataset (encoded and with no missing values).


              
12345

            
import pandas as pd

df = pd.read_csv('https://codefinity-content-media.s3.eu-west-1.amazonaws.com/a65bbc96-309e-4df9-a790-a1eb8c815a1c/penguins_imputed_encoded.csv')

print(df)
copy

Here is a little reminder of StandardScaler class.

Tarefa

  1. Import the StandardScaler class
  2. Initialize a StandardScaler object.
  3. Fit and transform the X using that object.

Switch to desktopMude para o desktop para praticar no mundo realContinue de onde você está usando uma das opções abaixo
Seção 2. Capítulo 11
Switch to desktopMude para o desktop para praticar no mundo realContinue de onde você está usando uma das opções abaixo
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