Building, Training, and Validating Models in Microsoft Azure

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Bismark Adomako

1:40:11

18 View

01 - Course Overview

01 - Course Overview.mp4

01:11

02 - Creating a Hypothesis

02 - Overview.mp4

01:42

03 - Factors of a Well Defined Hypothesis.mp4

02:51

04 - Alternative Hypothesis and Null Hypothesis.mp4

03:49

05 - Key Performance Indicators Required.mp4

01:35

06 - Hypothesis Acceptance Criteria.mp4

01:47

07 - Summary.mp4

00:44

03 - Sourcing and Transforming Data Relevant to a Hypothesis

08 - Overview.mp4

00:44

09 - Azure Machine Learning Services Overview.mp4

04:19

10 - Identifying and Sourcing the Relevant Data.mp4

03:07

11 - Training and Testing Data Subsets.mp4

03:25

12 - Data Transformation Using Asure Machine Learning components.mp4

06:57

13 - Summery.mp4

00:44

04 - Identifying Features from Raw Data

14 - overview.mp4

01:01

15 - Selecting Data Granuiarity.mp4

05:27

16 - Dependent and Independent Variables.mp4

02:46

17 - Choosing the Right Encoding Method.mp4

03:20

18 - Feature Scaling.mp4

02:51

19 - Common Feature Selection Techniques.mp4

09:02

20 - Summary.mp4

00:46

05 - Building the Model

21 - Overview.mp4

01:07

22 - Selecting the Appropriate Model.mp4

04:45

23 - Azure ML Classification Model Options.mp4

10:26

24 - Metrics for Model Classification and Evaiuation.mp4

07:10

25 - Summary.mp4

00:45

06 - Monitoring and Managing the Performance of a Model

26 - Overview.mp4

00:41

27 - Deploying Model as Betch Inference.mp4

04:18

28 - Deploying Mode as Real-Time Inference End Points.mp4

04:38

29 - Model Performance Strategy.mp4

07:03

30 - Summary.mp4

01:10

Description

This course gives Microsoft Azure Data Scientists a road map on how to build, train, and validate machine learning models in Azure.

What You'll Learn?

Building machine learning models in Microsoft Azure can appear intimidiating. This course, Building, Training, and Validating Models in Microsoft Azure, will help you decide which model to choose and why by building a model which will try to predict if a flight would be delayed more than 15 mins with given data. First, you will go through a real world problem to see how Azure ML can solve this problem, helping you form a hypothesis on which the model performance can be judged.

Next, you will quickly get Azure ML set up and learn why you need to split data for training and testing the models.

Then, you will explore the dependent and independent variables, which independent variables should be picked, why they should be picked, as well as feature data conversion such as label encoding and feature scaling.

Finally, you will discover which models to choose and why before obtaining the score of the model which will show how we can optimize the model and re-test.

When you are finished with this course, you will be ready to put your own model into production and monitor and retrain that model when necessary.

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Machine Learning

Microsoft Azure

Bismark Adomako

Instructor's Courses

Bismark believes that education is no longer a one-time investment, but instead a lifelong pursuit and that great authors and mentors can play an invaluable role in finding and developing a fulfilling career. He holds a Bachelor of Science degree in Computer Engineering specializing in Software Engineering, Artificial Intelligence and Distributed computing. He taught courses in robotics and computer vision as a student leader whiles in school, and interned and consulted for the British Council as a Software Engineer and Project Assistant. He currently works as a Business Intelligence and Big Data developer doubling up as a Data Scientist at Ecobank eProcess International S.A. developing machine learning models some of which includes the Customer360 model consisting of a customer segmentation model, customer churn model, and customer cross selling (product recommendation) model. He has enterprise level knowledge in data integration for BI & Data Management domain applications.

Pluralsight

View courses Pluralsight

Pluralsight, LLC is an American privately held online education company that offers a variety of video training courses for software developers, IT administrators, and creative professionals through its website. Founded in 2004 by Aaron Skonnard, Keith Brown, Fritz Onion, and Bill Williams, the company has its headquarters in Farmington, Utah. As of July 2018, it uses more than 1,400 subject-matter experts as authors, and offers more than 7,000 courses in its catalog. Since first moving its courses online in 2007, the company has expanded, developing a full enterprise platform, and adding skills assessment modules.