$990

August: Grow your customer base with Azure Machine Learning

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Location

BizData Head Office

Level 9

85 Queen Street

Melbourne, Victoria 3000

Australia

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Refund Policy

Refund Policy

No Refunds

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Event description

Description

Move beyond retrospective dashboards and start building meaningful data products for your business.

Building on our introductory course 'Event Detection with Azure Machine Learning', this course focuses on finding more customers who look like your most valuable customers, and recommending the most appropriate products to maximise their value.

Covering the following topics, this class includes practical labs using Azure Machine Learning, Power BI Desktop and R client to demonstrate the lifecycle of designing your predictive solution.

Topics Covered:

1. Review of machine learning

2. Segmentation and recommendation systems

3. Data for clustering

4. Build a segmentation

5. Understanding clustering using visualisation

6. Data for recommendation systems

7. Build a recommender

8. Deploy Azure ML models


Hands-on Lab Prerequisites



You will need to bring along your own laptop and ensure the following:

1. Download and Install Power BI Desktop (https://powerbi.microsoft.com/en-us/desktop/)

Prerequisites:

· Windows 7 / Windows Server 2008 R2 or later

· .NET 4.5

· Internet Explorer 9 or later

· Memory (RAM):

At least 1 GB available, 1.5 GB or more recommended

· Display:

At least 1440 x 900 or 1600 x 900 (16:9) recommended. Lower resolutions are not recommended as certain controls display beyond those resolutions

· CPU:

1 GHz or faster x86 or x64-bit processor recommended

**You will need to make sure WIFI access on your machine will support internet access during the session.

2. Download and Install R Client for Power

BI Desktop (https://powerbi.microsoft.com/en-us/documentation/powerbi-desktop-r-scripts/)


Make sure you register now to secure your spot as seats are limited.

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Date and Time

Location

BizData Head Office

Level 9

85 Queen Street

Melbourne, Victoria 3000

Australia

View Map

Refund Policy

No Refunds

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