Tensorflow: K-means Clustering with TensorFlow


Overview/Description
Expected Duration
Lesson Objectives
Course Number
Expertise Level



Overview/Description

Discover how to differentiate between supervised and unsupervised machine learning techniques. The construction of clustering models and their application to classification problems is also covered.



Expected Duration (hours)
1.0

Lesson Objectives

Tensorflow: K-means Clustering with TensorFlow

  • distinguish between supervised and unsupervised learning algorithms
  • identify the characteristics of supervised learning algorithms
  • identify the characteristics of unsupervised learning algorithms
  • recognize use cases where unsupervised learning can be applied
  • define the objectives of clustering algorithms
  • describe the process of k-means clustering to group data
  • describe how to implement k-means clustering
  • recall how to install TensorFlow and work with Jupyter notebooks
  • generate random data for clustering algorithms
  • perform k-means clustering using a TensorFlow estimator
  • explore the Iris dataset of flowers
  • perform clustering and classification on the Iris dataset
  • recall characteristics of unsupervised learning algorithms
  • describe the process and use cases of clustering
  • Course Number:
    it_sdaidt_07_enus

    Expertise Level
    Intermediate