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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

bookCreate a Complete ML Pipeline

Now let's build a proper pipeline with the final estimator. As a result, we will get a trained prediction pipeline that can be used for predicting new instances simply by calling the .predict() method.
To train a predictor (model), you need y to be encoded. This is done separately from the Pipeline we build for X.
Remember that LabelEncoder is used for encoding the target.

You have the same Penguins dataset.
The task is to build a pipeline with KNeighborsClassifier as a final estimator, train it, and predict values for the X itself.
Since the predictions will be encoded (0, 1, or 2), we will use the .inverse_transform() method of LabelEncoder to get the predictions back to 'Adelie', 'Chinstrap', or 'Gentoo'.

Task
test

Swipe to show code editor

  1. Encode the y variable using the LabelEncoder.
  2. Make a pipeline containing ct, SimpleImputer, StandardScaler, and KNeighborsClassifier (in that order).
  3. Train the pipe object.

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Section 3. Chapter 6
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bookCreate a Complete ML Pipeline

Now let's build a proper pipeline with the final estimator. As a result, we will get a trained prediction pipeline that can be used for predicting new instances simply by calling the .predict() method.
To train a predictor (model), you need y to be encoded. This is done separately from the Pipeline we build for X.
Remember that LabelEncoder is used for encoding the target.

You have the same Penguins dataset.
The task is to build a pipeline with KNeighborsClassifier as a final estimator, train it, and predict values for the X itself.
Since the predictions will be encoded (0, 1, or 2), we will use the .inverse_transform() method of LabelEncoder to get the predictions back to 'Adelie', 'Chinstrap', or 'Gentoo'.

Task
test

Swipe to show code editor

  1. Encode the y variable using the LabelEncoder.
  2. Make a pipeline containing ct, SimpleImputer, StandardScaler, and KNeighborsClassifier (in that order).
  3. Train the pipe 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 3. Chapter 6
toggle bottom row

bookCreate a Complete ML Pipeline

Now let's build a proper pipeline with the final estimator. As a result, we will get a trained prediction pipeline that can be used for predicting new instances simply by calling the .predict() method.
To train a predictor (model), you need y to be encoded. This is done separately from the Pipeline we build for X.
Remember that LabelEncoder is used for encoding the target.

You have the same Penguins dataset.
The task is to build a pipeline with KNeighborsClassifier as a final estimator, train it, and predict values for the X itself.
Since the predictions will be encoded (0, 1, or 2), we will use the .inverse_transform() method of LabelEncoder to get the predictions back to 'Adelie', 'Chinstrap', or 'Gentoo'.

Task
test

Swipe to show code editor

  1. Encode the y variable using the LabelEncoder.
  2. Make a pipeline containing ct, SimpleImputer, StandardScaler, and KNeighborsClassifier (in that order).
  3. Train the pipe 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!

Now let's build a proper pipeline with the final estimator. As a result, we will get a trained prediction pipeline that can be used for predicting new instances simply by calling the .predict() method.
To train a predictor (model), you need y to be encoded. This is done separately from the Pipeline we build for X.
Remember that LabelEncoder is used for encoding the target.

You have the same Penguins dataset.
The task is to build a pipeline with KNeighborsClassifier as a final estimator, train it, and predict values for the X itself.
Since the predictions will be encoded (0, 1, or 2), we will use the .inverse_transform() method of LabelEncoder to get the predictions back to 'Adelie', 'Chinstrap', or 'Gentoo'.

Task
test

Swipe to show code editor

  1. Encode the y variable using the LabelEncoder.
  2. Make a pipeline containing ct, SimpleImputer, StandardScaler, and KNeighborsClassifier (in that order).
  3. Train the pipe object.

Switch to desktopSwitch to desktop for real-world practiceContinue from where you are using one of the options below
Section 3. Chapter 6
Switch to desktopSwitch to desktop for real-world practiceContinue from where you are using one of the options below
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