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.
This sixth session in the series covered Clustering 102, where we learn more about the methods on multiple dimensions, how to compare Cluster techniques, and explore Dimensionality Reduction and how to extract only the most meaningful attributes from datasets with lots of attributes (or derived attributes).
Session highlights:
00:39 Oracle Machine Learning Office Hours - next Session
01:28 Machine Learning 102 - Clustering
02:00 Clustering 102 - Demo Introduction
03:25 Dataset for Demo
03:45 Dataset view
04:40 2-D visualization of subset of Dataset
06:40 k-Means model demo build with k=2 clusters
08:10 k-Means cluster prediction
09:15 k-Means prediction visualization in 2-D and 3-D
11:20 Identify Attributes that explain the prediction for cluster
13:15 Elbow Method to identify ideal number of clusters for k-Means
15:45 Function to build, score and plot k-Means clusters
16:49 Visualizing k-Means from k=2 to k=7 in 3-D
18:05 O-Cluster algorithm demo
21:45 Materializing OML4Py proxy object to a Database table
23:21 O-Cluster model build using PL/SQL
24:55 O-Cluster model settings and attributes
25:45 O-Cluster model views
27:52 O-Cluster cluster prediction
29:20 O-Cluster prediction visualization in 2-D and 3-D
32:00 Expectation-Maximization clustering demo introduction
33:10 E-M clustering model build
37:43 E-M clustering scoring and Attribute Importance
38:13 Identify Attributes that explain the prediction for cluster
39:14 Create a function to build, score and plot an E-M clustering model
39:38 Several E-M clustering results using several available settings
41:20 E-M clustering with Model Search Enable
45:19 E-M clustering models and model views
47:10 E-M clustering scoring via Python and SQL
50:55 Q&A
