Contenido del Curso

ML Introduction with scikit-learn

1. Machine Learning Concepts

What is ML Types of Machine Learning Training Set Types of Data Machine Learning Workflow

2. Preprocessing Data with Scikit-learn

Scikit-learn Concepts Getting Familiar with Dataset Dealing with Missing Values Challenge: Imputing Missing Values OrdinalEncoder One-Hot Encoder LabelEncoder Challenge: Encoding Categorical Variables Why Scale the Data?StandardScaler, MinMaxScaler, MaxAbsScaler Challenge: Scaling the Features Preprocessing Summary

3. Pipelines

What is Pipeline ColumnTransformer Efficient Data Preprocessing with Pipelines Challenge: Creating a Pipeline Final Estimator Challenge: Creating a Complete ML Pipeline

4. Modeling

Models KNeighborsClassifier Evaluating a Model. Train-Test split.Cross-Validation Evaluate the Model with Cross-Validation GridSearchCV The Flaw of GridSearchCV Tune Hyperparameters with RandomizedSearchCV Modeling Summary Putting It All Together

Challenge: Creating a Pipeline

In this challenge, you need to put all the preprocessing steps we did together into one pipeline. The dataset is the initial penguins.csv file we started from.

The first step is to remove two useless rows. Then you will have to create a pipeline containing encoding, imputing, and scaling.

You need to encode only two columns, 'sex' and 'island'. Since you do not want to encode the entire X, you must use a ColumnTransformer. Afterward, apply the SimpleImputer and StandardScaler to the entire X.

Here is a reminder of the make_column_transformer() and make_pipeline() functions you will use.

Tarea

Swipe to show code editor

Import the correct function for creating a pipeline.
Make a ColumnTransformer with the OneHotEncoder applied only to columns 'sex' and 'island'.
Make sure that all other columns remain untouched.
Create a pipeline containing ct you just created, SimpleImputer that fills in missing values with the most frequent value and a StandardScaler as a last step.
Transform the X using the pipe you created.

Cambia al escritorio para practicar en el mundo realContinúe desde donde se encuentra utilizando una de las siguientes opciones

¿Todo estuvo claro?

¡Gracias por tus comentarios!

Sección 3. Capítulo 4

Challenge: Creating a Pipeline

In this challenge, you need to put all the preprocessing steps we did together into one pipeline. The dataset is the initial penguins.csv file we started from.

The first step is to remove two useless rows. Then you will have to create a pipeline containing encoding, imputing, and scaling.

Here is a reminder of the make_column_transformer() and make_pipeline() functions you will use.

Tarea

Swipe to show code editor

Import the correct function for creating a pipeline.
Make a ColumnTransformer with the OneHotEncoder applied only to columns 'sex' and 'island'.
Make sure that all other columns remain untouched.
Create a pipeline containing ct you just created, SimpleImputer that fills in missing values with the most frequent value and a StandardScaler as a last step.
Transform the X using the pipe you created.

Cambia al escritorio para practicar en el mundo realContinúe desde donde se encuentra utilizando una de las siguientes opciones

¿Todo estuvo claro?

¡Gracias por tus comentarios!

Sección 3. Capítulo 4

Challenge: Creating a Pipeline

In this challenge, you need to put all the preprocessing steps we did together into one pipeline. The dataset is the initial penguins.csv file we started from.

The first step is to remove two useless rows. Then you will have to create a pipeline containing encoding, imputing, and scaling.

Here is a reminder of the make_column_transformer() and make_pipeline() functions you will use.

Tarea

Swipe to show code editor

Import the correct function for creating a pipeline.
Make a ColumnTransformer with the OneHotEncoder applied only to columns 'sex' and 'island'.
Make sure that all other columns remain untouched.
Create a pipeline containing ct you just created, SimpleImputer that fills in missing values with the most frequent value and a StandardScaler as a last step.
Transform the X using the pipe you created.

Cambia al escritorio para practicar en el mundo realContinúe desde donde se encuentra utilizando una de las siguientes opciones

¿Todo estuvo claro?

¡Gracias por tus comentarios!

In this challenge, you need to put all the preprocessing steps we did together into one pipeline. The dataset is the initial penguins.csv file we started from.

The first step is to remove two useless rows. Then you will have to create a pipeline containing encoding, imputing, and scaling.

Here is a reminder of the make_column_transformer() and make_pipeline() functions you will use.

Tarea

Swipe to show code editor

Import the correct function for creating a pipeline.
Make a ColumnTransformer with the OneHotEncoder applied only to columns 'sex' and 'island'.
Make sure that all other columns remain untouched.
Create a pipeline containing ct you just created, SimpleImputer that fills in missing values with the most frequent value and a StandardScaler as a last step.
Transform the X using the pipe you created.

Cambia al escritorio para practicar en el mundo realContinúe desde donde se encuentra utilizando una de las siguientes opciones

Sección 3. Capítulo 4

Cambia al escritorio para practicar en el mundo realContinúe desde donde se encuentra utilizando una de las siguientes opciones