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
Course Overview
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