ML Introduction with scikit-learn

ML Introduction with scikit-learn

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1. Machine Learning Concepts

Learn the Machine Learning concepts and the ML project workflow.

Types of Machine Learning

Machine Learning Workflow

2. Preprocessing Data with Scikit-learn

Preprocessing is probably the most important stage of an ML project. This chapter covers the preprocessing steps needed for almost any dataset.

Scikit-learn Concepts

Getting Familiar with Dataset

Dealing with Missing Values

Challenge: Imputing Missing Values

One-Hot Encoder

Challenge: Encoding Categorical Variables

Why Scale the Data?

StandardScaler, MinMaxScaler, MaxAbsScaler

Challenge: Scaling the Features

Preprocessing Summary

3. Pipelines

A pipeline is a neat way to combine all the preprocessing steps as well as a model. Pipelines make it much easier to train and use a model.

What is Pipeline

ColumnTransformer

Efficient Data Preprocessing with Pipelines

Challenge: Creating a Pipeline

Final Estimator

Challenge: Creating a Complete ML Pipeline

4. Modeling

Modeling is the most fun stage of an ML project. Let's learn to build, fine-tune and evaluate the model!

KNeighborsClassifier

Evaluating the Model

Cross-Validation

Challenge: Evaluating the Model with Cross-Validation

The Flaw of GridSearchCV

Challenge: Tuning Hyperparameters with RandomizedSearchCV

Modeling Summary

Challenge: Putting It All Together

Зміст курсу

ML Introduction with scikit-learn

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 the Model Cross-Validation Challenge: Evaluating the Model with Cross-Validation GridSearchCV The Flaw of GridSearchCV Challenge: Tuning Hyperparameters with RandomizedSearchCV Modeling Summary Challenge: 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.

Завдання

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.

Перейдіть на комп'ютер для реальної практикиПродовжуйте з того місця, де ви зупинились, використовуючи один з наведених нижче варіантів

Все було зрозуміло?

Дякуємо за ваш відгук!

Секція 3. Розділ 4

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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.

Завдання

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.

Перейдіть на комп'ютер для реальної практикиПродовжуйте з того місця, де ви зупинились, використовуючи один з наведених нижче варіантів

Все було зрозуміло?

Дякуємо за ваш відгук!

Секція 3. Розділ 4

Перейдіть на комп'ютер для реальної практикиПродовжуйте з того місця, де ви зупинились, використовуючи один з наведених нижче варіантів

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