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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 ValuesChallenge: Imputing Missing ValuesOrdinalEncoderOne-Hot EncoderLabelEncoderChallenge: Encoding Categorical VariablesWhy Scale the Data?StandardScaler, MinMaxScaler, MaxAbsScalerChallenge: Scaling the FeaturesPreprocessing Summary
3. Pipelines
What is PipelineColumnTransformerEfficient Data Preprocessing with PipelinesChallenge: Creating a PipelineFinal EstimatorChallenge: Creating a Complete ML Pipeline
4. Modeling
ModelsKNeighborsClassifierEvaluating the ModelCross-ValidationChallenge: Evaluating the Model with Cross-ValidationGridSearchCVThe Flaw of GridSearchCVChallenge: Tuning Hyperparameters with RandomizedSearchCVModeling SummaryChallenge: Putting It All Together

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Challenge: Putting It All Together

In this challenge, you will apply everything you learned throughout the course from data preprocessing to training and evaluating the model.

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Task
test

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  1. Encode the target.
  2. Split the data so that 33% is used for the test set and the remainder for the training set.
  3. Make a ColumnTransformer to encode only the 'island' and 'sex' columns. Make sure the others columns remain untouched. Use a proper encoder for nominal data.
  4. Fill the gaps in a param_grid to try the following values for the number of neighbors: [1, 3, 5, 7, 9, 12, 15, 20, 25].
  5. Create a GridSearchCV object with the KNeighborsClassifier as a model.
  6. Construct a pipeline that begins with ct as the first step, followed by imputation using the most frequent value, standardization, and concludes with GridSearchCV as the final estimator.
  7. Train the model using a pipeline on the training set.
  8. Evaluate the model on the test set. (Print its score)
  9. Get a predicted target for X_test.
  10. Print the best estimator found by grid_search.

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Section 4. Chapter 10
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book
Challenge: Putting It All Together

In this challenge, you will apply everything you learned throughout the course from data preprocessing to training and evaluating the model.

carousel-imgcarousel-imgcarousel-imgcarousel-imgcarousel-img
Task
test

Swipe to show code editor

  1. Encode the target.
  2. Split the data so that 33% is used for the test set and the remainder for the training set.
  3. Make a ColumnTransformer to encode only the 'island' and 'sex' columns. Make sure the others columns remain untouched. Use a proper encoder for nominal data.
  4. Fill the gaps in a param_grid to try the following values for the number of neighbors: [1, 3, 5, 7, 9, 12, 15, 20, 25].
  5. Create a GridSearchCV object with the KNeighborsClassifier as a model.
  6. Construct a pipeline that begins with ct as the first step, followed by imputation using the most frequent value, standardization, and concludes with GridSearchCV as the final estimator.
  7. Train the model using a pipeline on the training set.
  8. Evaluate the model on the test set. (Print its score)
  9. Get a predicted target for X_test.
  10. Print the best estimator found by grid_search.

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 4. Chapter 10
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