Unsupervised-Learning

I am new to Unsupervised learning. I have used models from AWS SageMaker such as K-Means and RCF, fit my input data and observe results. These are simple beginner level models and focussed on execution in Sagemaker.

K-Means Clustering (From AWS Sagemaker samples)

Classify handwritten digits, without ground truth, from MNIST through K-means clustering. In this method, each point belongs to the cluster with the closest mean, and the data is partitioned into a number of clusters that is specified when framing the problem. In this case, since we know there are 10 clusters, and we have no labeled data (in the way we framed the problem), this is a good fit.

Steps -

Data ingestion

Data inspection

Data conversion

Training the K-Means model

Set up hosting for the model

Validate the model for use

Random Cut Forest (From AWS Sagemaker samples)

To be updated