Über diesen Kurs

57,437 kürzliche Aufrufe

This course covers the theoretical foundation for different techniques associated with supervised machine learning models. In addition, a business case study is defined to guide participants through all steps of the analytical life cycle, from problem understanding to model deployment, through data preparation, feature selection, model training and validation, and model assessment. A series of demonstrations and exercises is used to reinforce the concepts and the analytical approach to solving business problems. 

This course uses Model Studio, the pipeline flow interface in SAS Viya that enables you to prepare, develop, compare, and deploy advanced analytics models. You learn to train supervised machine learning models to make better decisions on big data. The SAS applications used in this course make machine learning possible without programming or coding.

100 % online

Beginnen Sie sofort und lernen Sie in Ihrem eigenen Tempo.

Flexible Fristen

Setzen Sie Fristen gemäß Ihrem Zeitplan zurück.

Stufe „Mittel“

Ca. 27 Stunden zum Abschließen

Englisch

Untertitel: Englisch

100 % online

Beginnen Sie sofort und lernen Sie in Ihrem eigenen Tempo.

Flexible Fristen

Setzen Sie Fristen gemäß Ihrem Zeitplan zurück.

Stufe „Mittel“

Ca. 27 Stunden zum Abschließen

Englisch

Untertitel: Englisch

Lehrplan - Was Sie in diesem Kurs lernen werden

Woche

1

1 Stunde zum Abschließen

Course Overview

In this module, you meet the instructor and learn about course logistics, such as how to access the software for this course.

1 Video (Gesamt 1 min), 3 Lektüren, 1 Quiz

1 Video

Welcome to the Course!1m

3 Lektüren

Learner Prerequisites

Using SAS® Viya® for Learners with This Course (Required)10m

Using Forums and Getting Help10m

5 Stunden zum Abschließen

Getting Started with Machine Learning using SAS® Viya®

In this module, you learn how you can meet today's business challenges with machine learning using SAS® Viya®. You start working on the project that runs throughout the course.

15 Videos (Gesamt 40 min), 16 Lektüren, 10 Quiz

15 Videos

Introduction16

Machine Learning in SAS Viya2m

Analytics Life Cycle1m

Case Study: Customer Churn2m

SAS Viya Tools for SAS Visual Data Mining and Machine Learning1m

Demo: Creating a Project4m

Predictive Modeling5m

Importance of Data Preparation55

Essential Data Tasks1m

Dividing the Data3m

Addressing Rare Events Using Event-Based Sampling3m

Demo: Modifying the Data Partition4m

Managing Missing Values3m

Demo: Building a Pipeline from a Basic Template4m

SAS Viya in the SAS Platform: Architecture1m

16 Lektüren

Applications of Prediction-Based Decision Making10m

Advantages of the SAS Platform10m

Case Study: Data Dictionary10m

SAS Drive and the Applications Menu10m

Importing Data from a Local Source10m

SAS Viya Tools for Data Preparation10m

Cross Validation for Small Data Sets10m

Global Metadata10m

Managing Missing Values: Details10m

Pipeline Templates in Model Studio10m

Logistic Regression10m

SAS Cloud Analytic Services10m

SAS Viya: A Shift in Mindset10m

Data Sources and CAS10m

Interfaces and Products10m

SAS Visual Data Mining and Machine Learning10m

7 praktische Übungen

Question 1.012m

Question 1.022m

Question 1.032m

Question 1.042m

Question 1.052m

Question 1.062m

Getting Started with Machine Learning and SAS Viya30m

Woche

2

6 Stunden zum Abschließen

Data Preparation and Algorithm Selection

In this module, you learn to explore the data and finish preparing the data for analysis. You also learn some general considerations for selecting an algorithm.

14 Videos (Gesamt 47 min), 11 Lektüren, 16 Quiz

14 Videos

Introduction to Data Preparation and Algorithm Selection48

Exploring the Data1m

Demo: Exploring the Data4m

Replacing Incorrect Values1m

Demo: Replacing Incorrect Values Starting on the Data Tab7m

Feature Creation27

Text Mining1m

Demo: Adding Text Mining Features7m

Using Transformations to Handle Extreme or Unusual Values3m

Demo: Transforming Inputs5m

Selecting Useful Inputs4m

Demo: Selecting Features6m

Demo: Saving a Pipeline to the Exchange1m

Essential Discovery Tasks and Selecting an Algorithm1m

11 Lektüren

Data Mining Preprocessing Nodes in Model Studio10m

Replacing Incorrect Values Starting with the Manage Variables Node10m

Singular Value Decomposition10m

Feature Extraction Node10m

Finding the Best Transformation in Model Studio10m

Feature Selection and the Variable Selection Node in Model Studio: Details10m

Variable Clustering10m

Best Practices for Common Data Preparation Challenges10m

Automated Feature Engineering Pipeline Template10m

Considerations for Selecting an Algorithm10m

Comparison of Modeling Algorithms10m

9 praktische Übungen

Question 2.012m

Question 2.022m

Question 2.032m

Question 2.042m

Question 2.052m

Question 2.062m

Question 2.072m

Question 2.085m

Data Preparation and Algorithm Selection Quiz30m

Woche

3

7 Stunden zum Abschließen

Decision Trees and Ensembles of Trees

In this module, you learn to build decision tree models as well as models based on ensembles, or combinations, of decision trees.

23 Videos (Gesamt 68 min), 12 Lektüren, 21 Quiz

23 Videos

Introduction to Decision Trees and Ensembles of Trees41

Basics of Decision Trees2m

Demo: Building a Decision Tree Model Using the Default Settings7m

Decision Trees for Categorical Targets: Classification Trees3m

Decision Trees for Interval Targets: Regression Trees2m

Improving the Decision Tree Model25

Demo: Modifying the Structure Parameters1m

Recursive Partitioning3m

Splitting Criteria4m

Split Search9m

Demo: Modifying the Recursive Partitioning Parameters1m

Optimizing the Complexity of a Decision Tree Model39

Pruning3m

Demo: Modifying the Pruning Parameters2m

Regularizing and Tuning the Hyperparameters of a Machine Learning Model2m

Building Ensemble Models1m

Perturb and Combine Methods5m

Bagging2m

Boosting1m

Comparison of Tree-Based Models1m

Demo: Building a Gradient Boosting Model3m

Forest Models3m

Demo: Building a Forest Model4m

12 Lektüren

Impurity Reduction Measures for Categorical and Interval Targets10m

Splitting Criteria in Model Studio10m

Adjustments in a Split Search10m

Missing Values in Decision Trees in Model Studio10m

Surrogate Splits10m

Calculating Variable Importance for Surrogate Splits10m

Bottom-Up Pruning Requirements10m

Pruning Options in Model Studio10m

Autotuning Options for Decision Trees in Model Studio10m

Gradient Boosting Models10m

Autotuning Options for Gradient Boosting in Model Studio10m

Autotuning Options for Forests in Model Studio10m

11 praktische Übungen

Question 3.01

Question 3.022m

Question 3.032m

Question 3.042m

Question 3.052m

Think About It2m

Question 3.062m

Question 3.072m

Question 3.08

Question 3.092m

Decision Trees and Ensembles of Trees Quiz30m

Woche

4

4 Stunden zum Abschließen

Neural Networks

In this module, you learn to build neural network models.

18 Videos (Gesamt 37 min), 10 Lektüren, 13 Quiz

18 Videos

Introduction to Neural Networks29

Beyond Traditional Regression: Neural Networks3m

Limitations of Neural Networks2m

Basics of Neural Networks3m

Estimating Weights and Making Predictions3m

Learning Process2m

Essential Discovery Tasks for Neural Networks24

Demo: Building a Neural Network Using the Default Settings3m

Improving the Neural Network Model22

Neural Network Architectures4m

Activation Functions1m

Shaping the Sigmoid2m

Demo: Modifying the Neural Network Architecture1m

Optimizing the Complexity of a Neural Network Model40

Weight Decay1m

Early Stopping2m

Regularizing and Tuning the Hyperparameters of a Neural Network Model32

Demo: Modifying the Learning and Optimization Parameters2m

10 Lektüren

Standardization Methods10m

Iterative Updating in Numerical Optimization10m

Numerical Optimization Methods in Model Studio10m

Deviance Measures in Model Studio10m

Calculating the Number of Parameters10m

Deep Learning10m

Hidden Layer Activation Functions in Model Studio10m

Target Layer Activation Functions and Error Functions in Model Studio10m

Selected Hyperparameters Related to the Learning Process in Model Studio10m

Autotuning Options for Neural Networks in Model Studio10m

8 praktische Übungen

Question 4.012m

Question 4.022m

Question 4.032m

Question 4.042m

Question 4.052m

Question 4.062m

Question 4.072m

Neural Networks Quiz30m

Woche

5

4 Stunden zum Abschließen

Support Vector Machines

In this module, you learn to build support vector machine models.

17 Videos (Gesamt 26 min), 5 Lektüren, 11 Quiz

17 Videos

Introduction to Support Vector Machines46

Support Vector Machines as Classifier Models2m

Mathematical Definition of a Support Vector Machine1m

Maximum-Margin Hyperplane and Support Vectors1m

Essential Discovery Tasks for Support Vector Machines23

Demo: Building a Support Vector Machine Using the Default Settings2m

Improving the Support Vector Machine Model1m

Optimization Problem1m

Accounting for Errors with Nonlinearly Separable Data51

Demo: Modifying the Methods of Solution Parameters1m

Optimizing the Complexity of the Support Vector Machine Model31

Feature Space Approach for Nonlinearly Separable Data1m

Kernel Trick1m

Demo: Increasing the Flexibility of the Support Vector Machine2m

Model Interpretability1m

Demo: Adding Model Interpretability4m

Regularizing and Tuning the Hyperparameters of the Support Vector Machine Model22

5 Lektüren

Dot Products10m

Constraints for Optimization10m

Lagrange Approach for Estimation10m

Model Interpretability Plots10m

Autotuning Options for Support Vector Machines in Model Studio10m

5 praktische Übungen

Question 5.012m

Question 5.022m

Question 5.032m

Question 5.042m

Support Vector Machines Quiz30m

Woche

6

4 Stunden zum Abschließen

Model Deployment

In this module, you learn how to select the model that best meets the requirements of your business challenge and put the model into production. You also learn about managing the model over time.

14 Videos (Gesamt 27 min), 2 Lektüren, 11 Quiz

14 Videos

Introduction to Model Deployment36

Essential Deployment Tasks41

Selecting a Model45

Numeric Measures of Model Performance1m

Confusion Matrix for Decision Predictions1m

ROC Charts and the C-Statistic3m

Charts Based on Response Rate: CPH and Lift3m

Ways of Comparing Models in Model Studio1m

Demo: Comparing Models within a Pipeline2m

Demo: Comparing Models across Pipelines2m

Demo: Exploring the Settings for Model Comparison and Selection2m

Scoring and Managing the Champion Model1m

Demo: Exploring the Features for Scoring and Managing a Model in Model Manager2m

Monitoring and Updating the Model1m

2 Lektüren

Numeric Measures of Model Performance by Prediction Type10m

Model Selection Statistics by Target Type10m

6 praktische Übungen

Question 6.012m

Question 6.022m

Question 6.032m

Question 6.042m

Question 6.052m

Model Deployment Quiz30m

Dozenten

Jeff Thompson

Senior Analytical Training Consultant

Education

Catherine Truxillo

Director, Analytical Education

Education

Häufig gestellte Fragen

Wann erhalte ich Zugang zu den Vorträgen und Aufgaben?
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Wenn Sie ein Zertifikat erwerben, erhalten Sie Zugriff auf alle Kursmaterialien, einschließlich bewerteter Aufgaben. Nach Abschluss des Kurses wird Ihr elektronisches Zertifikat zu Ihrer Seite „Errungenschaften“ hinzugefügt – von dort können Sie Ihr Zertifikat ausdrucken oder es zu Ihrem LinkedIn Profil hinzufügen. Wenn Sie nur lesen und den Inhalt des Kurses anzeigen möchten, können Sie kostenlos als Gast an dem Kurs teilnehmen.
Wie erfolgen Rückerstattungen?
Ist finanzielle Unterstützung möglich?
What browser is recommended for accessing software in this course?
We recommend Chrome, Firefox or Safari when accessing the software.

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