Oracle Autonomous Datawarehouse Cloud - Machine Learning with Oracle - Forecasting example
Part 1
00:00 Welcome
00:26 Agenda
01:31 Example use case and problem statement
03:48 Data sources we will use
04:43 Machine learning theory - The "Naive" Model
05:28 Machine learning theory - Supervised Learning
08:00 Introduction to Autonomous Datawarehouse Cloud
09:24 Data collection - introduction
10:23 Data collection - DataSync demo
Part 2
11:33 Create prediction model with ADWC - Introduction
12:28 Create prediction model with ADWC - Combine the sources
15:08 Create prediction model with ADWC - Feature engineering
16:46 Create prediction model with ADWC - Validation theory
18:48 Create prediction model with ADWC - Syntax to create the model
21:35 Create prediction model with ADWC - Actual validation, visual approach
24:02 Create prediction model with ADWC - Actual validation, numerical approach
Part 3
26:08 Running the prediction model - A notebook to predict tomorrow's sales
27:07 Running the prediction model with ADWC - Automatically run it every day
27:22 Running the prediction model with ADWC - Embedding in an (APEX) application
29:05 Conclusions
31:23 How to get started
31:53 Questions and contact
When it comes to forecasting accuracy, machine Learning often outperforms the traditional models such as ARIMA. In this video we show you step-by-step how to use the power of machine learning for forecasting sales/demand.
This video is also a good way to learn how to work with machine learning in Autonomous Datawarehouse Cloud in general. The principles you learn here can be applied to many more machine learning use cases. We'll also cover any required ML theory, so you don't require any previous knowledge on this.
After watching this video you will have the basic ingredients to apply ML to your own business cases with Autonomous Datawarehouse Cloud.
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