Practitioner Level
12 Hands-on labs

DP-100 Part 2 - Modeling

By Brian Roehm

Learn to run experiments and train models as you prepare for Exam DP-100: Designing and Implementing a Data Science Solution on Azure.

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8 hours
  • 60 Lessons
  • 12 Hands-On Labs

About the course

In this course, we focus on how to run experiments and train models in Azure Machine Learning. This course is part two of a three part series, focusing on preparation for the DP-100 exam.

We examine how to:

  • Create models using Azure Machine Learning designer
  • Run training scripts in an Azure Machine Learning workspace
  • Generate metrics from an experiment run
  • Build a foundation using key algorithms, features, and machine learning models
  • Use important tools such as PyTorch, Scikit-learn, Keras, and Chainer

Lab Highlights

Developing with SMOTE in Azure Machine Learning Feature Selection After Training in Azure Machine Learning Feature Selection Before Training in Azure Machine Learning Classification Options in Azure Machine Learning Detecting Anomalies in Azure Machine Learning Creating Custom Modules in Azure Machine Learning Designer Exploring AML Designer Transforms: Edit Metadata and Convert to Indicator Values Exploring AML Designer Transforms: Apply SQL Transformation Exploring AML Designer Transforms: Join Data Exploring AML Designer Transforms: Apply Math Operation Exploring AML Designer Transforms: Add Columns Exploring AML Designer Transforms: Clean Missing Data
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  • Chapter 1 6 Lessons Course Introduction 12:50

    An Important Note About A Cloud Guru and Linux Academy Courses

    1:19

    Course Introduction

    3:09

    About the Training Architect

    1:05

    Using the DP-100 Essentials Guide

    1:59

    About the Exam

    2:50

    A Note on Data Science and Mathematics

    2:28
  • Chapter 2 11 Lessons Azure Machine Learning Pipelines 4:30:34

    A Refresh on Azure Machine Learning Pipelines

    5:35

    Designer Modules to Define Pipeline Data Flow

    13:30

    Using Custom Code Modules in Designer

    4:43

    Exam Essentials and References

    6:46

    Exploring AML Designer Transforms: Clean Missing Data

    15:00 Hands-On Lab

    Exploring AML Designer Transforms: Add Columns

    30:00 Hands-On Lab

    Exploring AML Designer Transforms: Apply Math Operation

    30:00 Hands-On Lab

    Exploring AML Designer Transforms: Join Data

    30:00 Hands-On Lab

    Exploring AML Designer Transforms: Apply SQL Transformation

    45:00 Hands-On Lab

    Exploring AML Designer Transforms: Edit Metadata and Convert to Indicator Values

    30:00 Hands-On Lab

    Creating Custom Modules in Azure Machine Learning Designer

    1:00:00 Hands-On Lab
  • Chapter 3 12 Lessons Machine Learning Algorithm 3:59:03

    An Introduction to Terminology

    15:10

    How to Select Algorithms in Azure Machine Learning

    8:02

    Text Analytics

    18:09

    Regression

    21:37

    Multiclass Classification

    23:55

    Image Classification

    11:30

    Anomaly Detection

    11:41

    Clustering

    9:53

    Recommenders

    8:32

    Exam Essentials and References

    5:34

    Anomaly Detection in Azure Machine Learning

    45:00 Hands-On Lab

    Classifications Options in Azure Machine Learning

    1:00:00 Hands-On Lab
  • Chapter 4 12 Lessons Feature Selection 2:51:40

    An Introduction to Feature Selection

    6:28

    Intro to Feature Extraction

    9:32

    Pearson's Correlation

    5:42

    Mutual Information Score

    9:14

    Kendall's Correlation Coefficient

    7:03

    Spearman's Correlation Coefficient

    7:55

    Chi-Squared Statistic

    4:49

    Fisher Score

    5:14

    Count-Based Feature Selection

    3:41

    Exam Essentials and References

    7:02

    Feature Selection Before Training in Azure Machine Learning

    1:00:00 Hands-On Lab

    Feature Selection After Training in Azure Machine Learning

    45:00 Hands-On Lab
  • Chapter 5 7 Lessons Classic Machine Learning Models 1:30:39

    Introduction to Neural Networks

    7:33

    RNN

    4:48

    DNN

    4:32

    CNN

    4:47

    SMOTE

    5:16

    Exam Essentials and References

    3:43

    Developing with SMOTE in Azure Machine Learning

    1:00:00 Hands-On Lab
  • Chapter 6 6 Lessons Run Training Scripts in an Azure Machine Learning Workspace 36:01

    Azure Machine Learning SDK Introduction

    6:54

    Create an Experiment with SDK

    4:43

    Consume Data from a Datastore with SDK

    6:56

    Consume Data from a Data Set with SDK

    8:55

    Choosing an Estimator in Azure Machine Learning

    5:56

    Exam Essentials and References

    2:37
  • Chapter 7 4 Lessons Generate Metrics from an Experiment Run 28:57

    Logging Metrics from an Experiment Run

    10:34

    Retrieving and Viewing Experiment Outputs

    7:09

    Using Logs to Troubleshoot Experiment Run Errors

    8:25

    Exam Essentials and References

    2:49
  • Chapter 8 2 Lessons Conclusion 7:00

    Review and Final Notes

    5:03

    What's Next

    1:57

What you will need

  • DP-100 Part 1 An Introduction to Azure Machine Learning

What are Hands-on Labs

What's the difference between theoretical knowledge and real skills? Practical real-world experience. That's where Hands-on Labs come in! Hands-on Labs are guided, interactive experiences that help you learn and practice real-world scenarios in real cloud environments. Hands-on Labs are seamlessly integrated in courses, so you can learn by doing.

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