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ML Introduction with scikit-learn
course content

Course Content

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.

Task

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

Switch to desktopSwitch to desktop for real-world practiceContinue from where you are using one of the options below
Everything was clear?

How can we improve it?

Thanks for your feedback!

Section 2. Chapter 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.

Task

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

Switch to desktopSwitch to desktop for real-world practiceContinue from where you are using one of the options below
Everything was clear?

How can we improve it?

Thanks for your feedback!

Section 2. Chapter 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.

Task

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

Switch to desktopSwitch to desktop for real-world practiceContinue from where you are using one of the options below
Everything was clear?

How can we improve it?

Thanks for your 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.

Task

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

Switch to desktopSwitch to desktop for real-world practiceContinue from where you are using one of the options below
Section 2. Chapter 11
Switch to desktopSwitch to desktop for real-world practiceContinue from where you are using one of the options below
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