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.

8 hours
  • 61 Lessons
  • 13 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
  • 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 12 Lessons Azure Machine Learning Pipelines 5:00:34

    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

    Exploring AML Designer Transforms: Clip 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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