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