Machine Learning 101: Regression

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 third chapter of the series “Oracle Machine Learning Office Hours – Machine Learning 101”.

In this "ML Regression 101" we learned how to set up a data set for regression modeling, build machine learning models that predict numeric values such as home prices, evaluate model quality and compare algorithms, as well as use AutoML for Regression.

Video Highlights
01:01 Next session announcement: 102 Regression
02:01 Machine Learning 101 - Regression
03:07 What is Regression
05:31 History of Regression
08:00 Data for our example
10:30 Terminology
11:11 Data preparation
13:43 Linear Regression model intuition
19:35 Model evaluation
22:47 AutoML
23:12 Demo for Machine Learning 101: Regression
23:50 Data Exploration
33:53 Regression model build
38:12 Linear Regression: Predicted vs. Actuals and Residuals
40:02 Computing the goodness-of-fit statistics in OML4Py
42:08 Linear Regression with feature creation and feature selection
43:57 Support Vector Machines Regression
47:35 Neural Networks Regression
49:25 Ranking of models so far
49:40 AutoML algorithm selection
51:04 AutoML GLM Ridge
52:37 Final ranking of models
53:20 Q&A

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