course content

Course Content

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 ValuesImputing ChallengeOrdinalEncoderOne-Hot EncoderLabelEncoderEncoding ChallengeWhy Scale the Data?StandardScaler, MinMaxScaler, MaxAbsScalerScaling ChallengePreprocessing Summary

3. Pipelines

What is PipelineColumnTransformerPipelineCreating a Pipeline ChallengeFinal EstimatorFinal Estimator Challenge

4. Modeling

ModelsKNeighborsClassifierEvaluating a Model. Train-Test split.Cross-ValidationCross-Validation ChallengeGridSearchCVThe Flaw of GridSearchCVRandomizedSearchCV. Hyperparameter Tuning ChallengeModeling SummaryPutting It All Together

ML Introduction with scikit-learn

Scaling ChallengeScaling Challenge

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).

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.

Everything was clear?

Section 2. Chapter 11
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