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: Encoding Categorical Variables

To summarize the previous three chapters, here is a table showing what encoder you should use:

In this challenge, you have the penguins dataset file (with no missing values). You need to deal with all the categorical values, including the target ('species' column).

Here is the reminder of the data you will work with:


              12345
            
import pandas as pd

df = pd.read_csv('https://codefinity-content-media.s3.eu-west-1.amazonaws.com/a65bbc96-309e-4df9-a790-a1eb8c815a1c/penguins_imputed.csv')

print(df.head())

Keep in mind that 'island' and 'sex' are categorical features and 'species' is a categorical target.

Завдання

Swipe to show code editor

Encode all the categorical values. For this, you need to choose the correct encoder for the 'island', and 'sex' columns and follow the steps.

Import the correct encoders for features and target.
Initialize the features encoder object.
Encode the categorical feature columns using the feature_enc object.
Initialize the target encoder object.
Encode the target using the label_enc object.

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 2. Розділ 8

toggle bottom row

Challenge: Encoding Categorical Variables

To summarize the previous three chapters, here is a table showing what encoder you should use:

In this challenge, you have the penguins dataset file (with no missing values). You need to deal with all the categorical values, including the target ('species' column).

Here is the reminder of the data you will work with:


              12345
            
import pandas as pd

df = pd.read_csv('https://codefinity-content-media.s3.eu-west-1.amazonaws.com/a65bbc96-309e-4df9-a790-a1eb8c815a1c/penguins_imputed.csv')

print(df.head())

Keep in mind that 'island' and 'sex' are categorical features and 'species' is a categorical target.

Завдання

Swipe to show code editor

Encode all the categorical values. For this, you need to choose the correct encoder for the 'island', and 'sex' columns and follow the steps.

Import the correct encoders for features and target.
Initialize the features encoder object.
Encode the categorical feature columns using the feature_enc object.
Initialize the target encoder object.
Encode the target using the label_enc object.

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 2. Розділ 8

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

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