Über diesen Kurs

48,710 kürzliche Aufrufe
Zertifikat zur Vorlage
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100 % online
Beginnen Sie sofort und lernen Sie in Ihrem eigenen Tempo.
Flexible Fristen
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Stufe „Mittel“
Ca. 47 Stunden zum Abschließen
Englisch
Zertifikat zur Vorlage
Erhalten Sie nach Abschluss ein Zertifikat
100 % online
Beginnen Sie sofort und lernen Sie in Ihrem eigenen Tempo.
Flexible Fristen
Setzen Sie Fristen gemäß Ihrem Zeitplan zurück.
Stufe „Mittel“
Ca. 47 Stunden zum Abschließen
Englisch

von

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SAS

Lehrplan - Was Sie in diesem Kurs lernen werden

Woche
1

Woche 1

1 Stunde zum Abschließen

Course Overview

1 Stunde zum Abschließen
1 Video (Gesamt 1 min), 3 Lektüren, 1 Quiz
3 Lektüren
Learner Prerequisites
Using SAS® Viya® for Learners with This Course (Required)10m
Using Forums and Getting Help10m
8 Stunden zum Abschließen

Getting Started with Machine Learning using SAS® Viya®

8 Stunden zum Abschließen
15 Videos (Gesamt 40 min), 16 Lektüren, 10 Quiz
15 Videos
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 1.0130m
Question 1.0230m
Question 1.0330m
Question 1.0430m
Question 1.0530m
Question 1.0630m
Getting Started with Machine Learning and SAS Viya30m
Woche
2

Woche 2

9 Stunden zum Abschließen

Data Preparation and Algorithm Selection

9 Stunden zum Abschließen
14 Videos (Gesamt 47 min), 11 Lektüren, 16 Quiz
14 Videos
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 2.0130m
Question 2.0230m
Question 2.0330m
Question 2.0430m
Question 2.0530m
Question 2.0630m
Question 2.0730m
Question 2.085m
Data Preparation and Algorithm Selection Quiz30m
Woche
3

Woche 3

11 Stunden zum Abschließen

Decision Trees and Ensembles of Trees

11 Stunden zum Abschließen
23 Videos (Gesamt 68 min), 12 Lektüren, 21 Quiz
23 Videos
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 3.0230m
Question 3.0330m
Question 3.0430m
Question 3.0530m
Think About It30m
Question 3.0630m
Question 3.0730m
Question 3.08
Question 3.0930m
Decision Trees and Ensembles of Trees Quiz30m
Woche
4

Woche 4

8 Stunden zum Abschließen

Neural Networks

8 Stunden zum Abschließen
18 Videos (Gesamt 37 min), 10 Lektüren, 13 Quiz
18 Videos
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 4.0130m
Question 4.0230m
Question 4.0330m
Question 4.0430m
Question 4.0530m
Question 4.0630m
Question 4.0730m
Neural Networks Quiz30m

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