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Machine Learning 101: Clustering

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Издатель
Have you always been curious about what machine learning can do for your business problem, but could never find the time to learn the practical necessary skills? Do you wish to learn what Classification, Regression, Clustering and Feature Extraction techniques do, and how to apply them using the Oracle Machine Learning family of products?

Join us for this special series “Oracle Machine Learning Office Hours – Machine Learning 101”, where we will go through the main steps of solving a Business Problem from beginning to end, using the different components available in Oracle Machine Learning: programming languages and interfaces, including Notebooks with SQL, UI, and languages like R and Python.

Our fifth session in the series will cover Clustering 101, where we will learn the terminology around Clustering or Segmentation, how to get the data prepared for clustering, how to measure cluster separation, identify potential pitfalls and see use cases.

We will continue to make use of the Oracle Machine Learning for Python (OML4Py) interface for the Autonomous Database.

Video Highlights
01:47 Machine Learning 101 - Clustering
02:45 What is Clustering?
04:40 Clustering Algorithms and Methods
07:49 Types of data needed for Clustering
09:30 Workflow and Data Preparation
14:05 Data used in the Demo
14:50 Clustering Model Intuition for k-Means
22:24 k-Means properties
25:53 Features of the Oracle Machine Learning clustering algorithms
28:22 Demo of Machine Learning 101: Clustering
30:24 Demo: selecting subset of data and filtering outliers
32:35 Demo: k-Means model with k=2
36:10 Demo: k-Means model prediction
38:45 Demo: Create a function for building, scoring and plotting k-Means
40:10 Demo: k-Means testing from k=2 to k=7
42:00 Demo: Expectation-Maximization
46:42 Demo: Create a function for building, scoring and plotting Expectation-Maximization
48:00 Demo: Expectation-Maximization testing with max clusters from 2 to 7
51:40 Q&A
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