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ML Introduction with scikit-learn
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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

Putting It All Together

In this challenge, you will apply everything you learned throughout the course. Here are the steps you need to take:

  1. Remove the rows that hold too little information;
  2. Encode the y;
  3. Split the dataset into training and test sets;
  4. Build a pipeline with all the preprocessing steps and the GridSearchCV as the final estimator to find the best hyperparameters;
  5. Train the model using the pipeline;
  6. Evaluate the model using the pipeline;
  7. Predict the target for X_new and decode it using the LabelEncoder's .inverse_transform().

Let's get to w̵o̵r̵k̵ code!

carousel-imgcarousel-imgcarousel-imgcarousel-imgcarousel-img

Завдання

  1. Encode the target using LabelEncoder.
  2. Split the data so that 33% is used for a test set and the rest – for a training set.
  3. Make a ColumnTransformer to encode only the 'island' and 'sex' columns. Make the others 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. Make a pipeline with ct as a first step and grid_search as a 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.

Завдання

  1. Encode the target using LabelEncoder.
  2. Split the data so that 33% is used for a test set and the rest – for a training set.
  3. Make a ColumnTransformer to encode only the 'island' and 'sex' columns. Make the others 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. Make a pipeline with ct as a first step and grid_search as a 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.

Все було зрозуміло?

Секція 4. Розділ 10
toggle bottom row

Putting It All Together

In this challenge, you will apply everything you learned throughout the course. Here are the steps you need to take:

  1. Remove the rows that hold too little information;
  2. Encode the y;
  3. Split the dataset into training and test sets;
  4. Build a pipeline with all the preprocessing steps and the GridSearchCV as the final estimator to find the best hyperparameters;
  5. Train the model using the pipeline;
  6. Evaluate the model using the pipeline;
  7. Predict the target for X_new and decode it using the LabelEncoder's .inverse_transform().

Let's get to w̵o̵r̵k̵ code!

carousel-imgcarousel-imgcarousel-imgcarousel-imgcarousel-img

Завдання

  1. Encode the target using LabelEncoder.
  2. Split the data so that 33% is used for a test set and the rest – for a training set.
  3. Make a ColumnTransformer to encode only the 'island' and 'sex' columns. Make the others 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. Make a pipeline with ct as a first step and grid_search as a 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.

Завдання

  1. Encode the target using LabelEncoder.
  2. Split the data so that 33% is used for a test set and the rest – for a training set.
  3. Make a ColumnTransformer to encode only the 'island' and 'sex' columns. Make the others 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. Make a pipeline with ct as a first step and grid_search as a 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.

Все було зрозуміло?

Секція 4. Розділ 10
toggle bottom row

Putting It All Together

In this challenge, you will apply everything you learned throughout the course. Here are the steps you need to take:

  1. Remove the rows that hold too little information;
  2. Encode the y;
  3. Split the dataset into training and test sets;
  4. Build a pipeline with all the preprocessing steps and the GridSearchCV as the final estimator to find the best hyperparameters;
  5. Train the model using the pipeline;
  6. Evaluate the model using the pipeline;
  7. Predict the target for X_new and decode it using the LabelEncoder's .inverse_transform().

Let's get to w̵o̵r̵k̵ code!

carousel-imgcarousel-imgcarousel-imgcarousel-imgcarousel-img

Завдання

  1. Encode the target using LabelEncoder.
  2. Split the data so that 33% is used for a test set and the rest – for a training set.
  3. Make a ColumnTransformer to encode only the 'island' and 'sex' columns. Make the others 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. Make a pipeline with ct as a first step and grid_search as a 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.

Завдання

  1. Encode the target using LabelEncoder.
  2. Split the data so that 33% is used for a test set and the rest – for a training set.
  3. Make a ColumnTransformer to encode only the 'island' and 'sex' columns. Make the others 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. Make a pipeline with ct as a first step and grid_search as a 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.

Все було зрозуміло?

In this challenge, you will apply everything you learned throughout the course. Here are the steps you need to take:

  1. Remove the rows that hold too little information;
  2. Encode the y;
  3. Split the dataset into training and test sets;
  4. Build a pipeline with all the preprocessing steps and the GridSearchCV as the final estimator to find the best hyperparameters;
  5. Train the model using the pipeline;
  6. Evaluate the model using the pipeline;
  7. Predict the target for X_new and decode it using the LabelEncoder's .inverse_transform().

Let's get to w̵o̵r̵k̵ code!

carousel-imgcarousel-imgcarousel-imgcarousel-imgcarousel-img

Завдання

  1. Encode the target using LabelEncoder.
  2. Split the data so that 33% is used for a test set and the rest – for a training set.
  3. Make a ColumnTransformer to encode only the 'island' and 'sex' columns. Make the others 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. Make a pipeline with ct as a first step and grid_search as a 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.

Секція 4. Розділ 10
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