About this Course

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Intermediate Level
Approx. 30 hours to complete
Subtitles: English
Shareable Certificate
Earn a Certificate upon completion
100% online
Start instantly and learn at your own schedule.
Flexible deadlines
Reset deadlines in accordance to your schedule.
Intermediate Level
Approx. 30 hours to complete
Subtitles: English

Offered by

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Syllabus - What you will learn from this course


Week 1

1 hour to complete

Course Overview

1 hour to complete
1 video (Total 1 min), 3 readings, 1 quiz
3 readings
Learner Prerequisites
Using SAS® Viya® for Learners with This Course (Required)10m
Using Forums and Getting Help10m
5 hours to complete

Getting Started with Machine Learning using SAS® Viya®

5 hours to complete
15 videos (Total 40 min), 16 readings, 10 quizzes
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 Preparation55s
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 readings
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 practice exercises
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

Week 2

6 hours to complete

Data Preparation and Algorithm Selection

6 hours to complete
14 videos (Total 47 min), 11 readings, 16 quizzes
14 videos
Exploring the Data1m
Demo: Exploring the Data4m
Replacing Incorrect Values1m
Demo: Replacing Incorrect Values Starting on the Data Tab7m
Feature Creation27s
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 readings
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 practice exercises
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

Week 3

7 hours to complete

Decision Trees and Ensembles of Trees

7 hours to complete
23 videos (Total 68 min), 12 readings, 21 quizzes
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 Model25s
Demo: Modifying the Structure Parameters1m
Recursive Partitioning3m
Splitting Criteria4m
Split Search9m
Demo: Modifying the Recursive Partitioning Parameters1m
Optimizing the Complexity of a Decision Tree Model39s
Demo: Modifying the Pruning Parameters2m
Regularizing and Tuning the Hyperparameters of a Machine Learning Model2m
Building Ensemble Models1m
Perturb and Combine Methods5m
Comparison of Tree-Based Models1m
Demo: Building a Gradient Boosting Model3m
Forest Models3m
Demo: Building a Forest Model4m
12 readings
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 practice exercises
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

Week 4

4 hours to complete

Neural Networks

4 hours to complete
18 videos (Total 37 min), 10 readings, 13 quizzes
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 Networks24s
Demo: Building a Neural Network Using the Default Settings3m
Improving the Neural Network Model22s
Neural Network Architectures4m
Activation Functions1m
Shaping the Sigmoid2m
Demo: Modifying the Neural Network Architecture1m
Optimizing the Complexity of a Neural Network Model40s
Weight Decay1m
Early Stopping2m
Regularizing and Tuning the Hyperparameters of a Neural Network Model32s
Demo: Modifying the Learning and Optimization Parameters2m
10 readings
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 practice exercises
Question 4.012m
Question 4.022m
Question 4.032m
Question 4.042m
Question 4.052m
Question 4.062m
Question 4.072m
Neural Networks Quiz30m



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