Course 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.
Learning 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