Contenido del Curso

Cluster Analysis

1. What is Clustering?

Formulation Of The Problem What Is Unique About Clustering?Types Of Clustering

2. Basic Clustering Algorithms

K-means Clustering Perform K-means Clustering Agglomerative Clustering Perform Agglomerative Clustering Mean Shift Clustering DBSCAN Clustering Perform DBSCAN Clustering Using Clustering On Real Data

3. How to choose the best model?

Internal Evaluation External Evaluation Quality Evaluation

Perform K-means Clustering

Tarea

Let's check the efficiency of the algorithm on different types of clusters. Now we will use the three built-in datasets of the sklearn library and try to use the K-means algorithm to cluster the corresponding points. We will provide visualizations and try to estimate the quality of clustering using these visualizations.

Your task is to use the K-means clustering algorithm and to solve 3 different clustering problems. Compare the results and make conclusions about clustering quality. You have to:

Use KMeans class from cluster module for import.
Use KMeans class to instantiate a class object
Use.fit()method to train model.
Use .labels_attribute to extract fitted clusters.

_{Once you've completed this task, click the button below the code to check your solution.}

Tarea

Your task is to use the K-means clustering algorithm and to solve 3 different clustering problems. Compare the results and make conclusions about clustering quality. You have to:

Use KMeans class from cluster module for import.
Use KMeans class to instantiate a class object
Use.fit()method to train model.
Use .labels_attribute to extract fitted clusters.

_{Once you've completed this task, click the button below the code to check your solution.}

Note
In visualizations, it is necessary to look not at the color of clusters, but at the relative position of points in real and predicted clusters (Python can color the same clusters with different colors in different pictures due to implementation features)

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

¿Todo estuvo claro?

Sección 2. Capítulo 2

Perform K-means Clustering

Tarea

Your task is to use the K-means clustering algorithm and to solve 3 different clustering problems. Compare the results and make conclusions about clustering quality. You have to:

Use KMeans class from cluster module for import.
Use KMeans class to instantiate a class object
Use.fit()method to train model.
Use .labels_attribute to extract fitted clusters.

_{Once you've completed this task, click the button below the code to check your solution.}

Tarea

Your task is to use the K-means clustering algorithm and to solve 3 different clustering problems. Compare the results and make conclusions about clustering quality. You have to:

Use KMeans class from cluster module for import.
Use KMeans class to instantiate a class object
Use.fit()method to train model.
Use .labels_attribute to extract fitted clusters.

_{Once you've completed this task, click the button below the code to check your solution.}

Note
In visualizations, it is necessary to look not at the color of clusters, but at the relative position of points in real and predicted clusters (Python can color the same clusters with different colors in different pictures due to implementation features)

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

¿Todo estuvo claro?

Sección 2. Capítulo 2

Perform K-means Clustering

Tarea

Your task is to use the K-means clustering algorithm and to solve 3 different clustering problems. Compare the results and make conclusions about clustering quality. You have to:

Use KMeans class from cluster module for import.
Use KMeans class to instantiate a class object
Use.fit()method to train model.
Use .labels_attribute to extract fitted clusters.

_{Once you've completed this task, click the button below the code to check your solution.}

Tarea

Your task is to use the K-means clustering algorithm and to solve 3 different clustering problems. Compare the results and make conclusions about clustering quality. You have to:

Use KMeans class from cluster module for import.
Use KMeans class to instantiate a class object
Use.fit()method to train model.
Use .labels_attribute to extract fitted clusters.

_{Once you've completed this task, click the button below the code to check your solution.}

Note
In visualizations, it is necessary to look not at the color of clusters, but at the relative position of points in real and predicted clusters (Python can color the same clusters with different colors in different pictures due to implementation features)

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

¿Todo estuvo claro?

Tarea

Your task is to use the K-means clustering algorithm and to solve 3 different clustering problems. Compare the results and make conclusions about clustering quality. You have to:

Use KMeans class from cluster module for import.
Use KMeans class to instantiate a class object
Use.fit()method to train model.
Use .labels_attribute to extract fitted clusters.

_{Once you've completed this task, click the button below the code to check your solution.}

Note
In visualizations, it is necessary to look not at the color of clusters, but at the relative position of points in real and predicted clusters (Python can color the same clusters with different colors in different pictures due to implementation features)

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

Sección 2. Capítulo 2

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