ML Introduction with scikit-learn

ML Introduction with scikit-learn

0%

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: Evaluating the Model with Cross-Validation

In this challenge, you will build and evaluate a model using both train-test evaluation and cross-validation. The data is an already preprocessed penguins dataset.

Here are some of the functions you will use:

Завдання

Swipe to show code editor

Your task is to create a 4-nearest neighbors classifier and first evaluate its performance using the cross-validation score. Then split the data into train-test sets, train the model on the training set, and evaluate its performance on the test set.

Initialize a KNeighborsClassifier with 4 neighbors.
Calculate the cross-validation scores of this model with the number of folds set to 3. You can pass an untrained model to a cross_val_score() function.
Use a suitable function to split X, y.
Train the model using the training set.
Evaluate the model using the test set.

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 4. Розділ 5

toggle bottom row

Challenge: Evaluating the Model with Cross-Validation

In this challenge, you will build and evaluate a model using both train-test evaluation and cross-validation. The data is an already preprocessed penguins dataset.

Here are some of the functions you will use:

Завдання

Swipe to show code editor

Your task is to create a 4-nearest neighbors classifier and first evaluate its performance using the cross-validation score. Then split the data into train-test sets, train the model on the training set, and evaluate its performance on the test set.

Initialize a KNeighborsClassifier with 4 neighbors.
Calculate the cross-validation scores of this model with the number of folds set to 3. You can pass an untrained model to a cross_val_score() function.
Use a suitable function to split X, y.
Train the model using the training set.
Evaluate the model using the test set.

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 4. Розділ 5

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

some-alt