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.
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Apprentissage automatique à l'aide de SAS Viya
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Résultats de carrière des étudiants
67%
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67%
a obtenu une augmentation de salaire ou une promotion
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Niveau intermédiaire
Approx. 48 heures pour terminer
Anglais
Sous-titres : Français, Portugais (européen), Russe, Anglais, Espagnol
Résultats de carrière des étudiants
67%
ont bénéficié d'un avantage concret dans leur carrières grâce à ce cours
67%
a obtenu une augmentation de salaire ou une promotion
Certificat partageable
Obtenez un Certificat lorsque vous terminez
100 % en ligne
Commencez dès maintenant et apprenez aux horaires qui vous conviennent.
Dates limites flexibles
Réinitialisez les dates limites selon votre disponibilité.
Niveau intermédiaire
Approx. 48 heures pour terminer
Anglais
Sous-titres : Français, Portugais (européen), Russe, Anglais, Espagnol
Offert par
SAS
Through innovative software and services, SAS empowers and inspires customers around the world to transform data into intelligence. SAS is a trusted analytics powerhouse for organizations seeking immediate value from their data. A deep bench of analytics solutions and broad industry knowledge keep our customers coming back and feeling confident. With SAS®, you can discover insights from your data and make sense of it all. Identify what’s working and fix what isn’t. Make more intelligent decisions. And drive relevant change.
Programme du cours : ce que vous apprendrez dans ce cours
1 heure pour terminer
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 heure pour terminer
1 vidéo (Total 1 min), 3 lectures, 1 quiz
1 vidéo
3 lectures
Learner Prerequisites
Using SAS® Viya® for Learners with This Course (Required)10 min
Using Forums and Getting Help10 min
8 heures pour terminer
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.
8 heures pour terminer
15 vidéos (Total 40 min), 16 lectures, 10 quiz
15 vidéos
Introduction16s
Machine Learning in SAS Viya2 min
Analytics Life Cycle1 min
Case Study: Customer Churn2 min
SAS Viya Tools for SAS Visual Data Mining and Machine Learning1 min
Demo: Creating a Project4 min
Predictive Modeling5 min
Importance of Data Preparation55s
Essential Data Tasks1 min
Dividing the Data3 min
Addressing Rare Events Using Event-Based Sampling3 min
Demo: Modifying the Data Partition4 min
Managing Missing Values3 min
Demo: Building a Pipeline from a Basic Template4 min
SAS Viya in the SAS Platform: Architecture1 min
16 lectures
Applications of Prediction-Based Decision Making10 min
Advantages of the SAS Platform10 min
Case Study: Data Dictionary10 min
SAS Drive and the Applications Menu10 min
Importing Data from a Local Source10 min
SAS Viya Tools for Data Preparation10 min
Cross Validation for Small Data Sets10 min
Global Metadata10 min
Managing Missing Values: Details10 min
Pipeline Templates in Model Studio10 min
Logistic Regression10 min
SAS Cloud Analytic Services10 min
SAS Viya: A Shift in Mindset10 min
Data Sources and CAS10 min
Interfaces and Products10 min
SAS Visual Data Mining and Machine Learning10 min
7 exercices pour s'entraîner
Question 1.0130 min
Question 1.0230 min
Question 1.0330 min
Question 1.0430 min
Question 1.0530 min
Question 1.0630 min
Getting Started with Machine Learning and SAS Viya30 min
9 heures pour terminer
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.
9 heures pour terminer
14 vidéos (Total 47 min), 11 lectures, 16 quiz
14 vidéos
Exploring the Data1 min
Demo: Exploring the Data4 min
Replacing Incorrect Values1 min
Demo: Replacing Incorrect Values Starting on the Data Tab7 min
Feature Creation27s
Text Mining1 min
Demo: Adding Text Mining Features7 min
Using Transformations to Handle Extreme or Unusual Values3 min
Demo: Transforming Inputs5 min
Selecting Useful Inputs4 min
Demo: Selecting Features6 min
Demo: Saving a Pipeline to the Exchange1 min
Essential Discovery Tasks and Selecting an Algorithm1 min
11 lectures
Data Mining Preprocessing Nodes in Model Studio10 min
Replacing Incorrect Values Starting with the Manage Variables Node10 min
Singular Value Decomposition10 min
Feature Extraction Node10 min
Finding the Best Transformation in Model Studio10 min
Feature Selection and the Variable Selection Node in Model Studio: Details10 min
Variable Clustering10 min
Best Practices for Common Data Preparation Challenges10 min
Automated Feature Engineering Pipeline Template10 min
Considerations for Selecting an Algorithm10 min
Comparison of Modeling Algorithms10 min
9 exercices pour s'entraîner
Question 2.0130 min
Question 2.0230 min
Question 2.0330 min
Question 2.0430 min
Question 2.0530 min
Question 2.0630 min
Question 2.0730 min
Question 2.085 min
Data Preparation and Algorithm Selection Quiz30 min
11 heures pour terminer
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.
11 heures pour terminer
23 vidéos (Total 68 min), 12 lectures, 21 quiz
23 vidéos
Basics of Decision Trees2 min
Demo: Building a Decision Tree Model Using the Default Settings7 min
Decision Trees for Categorical Targets: Classification Trees3 min
Decision Trees for Interval Targets: Regression Trees2 min
Improving the Decision Tree Model25s
Demo: Modifying the Structure Parameters1 min
Recursive Partitioning3 min
Splitting Criteria4 min
Split Search9 min
Demo: Modifying the Recursive Partitioning Parameters1 min
Optimizing the Complexity of a Decision Tree Model39s
Pruning3 min
Demo: Modifying the Pruning Parameters2 min
Regularizing and Tuning the Hyperparameters of a Machine Learning Model2 min
Building Ensemble Models1 min
Perturb and Combine Methods5 min
Bagging2 min
Boosting1 min
Comparison of Tree-Based Models1 min
Demo: Building a Gradient Boosting Model3 min
Forest Models3 min
Demo: Building a Forest Model4 min
12 lectures
Impurity Reduction Measures for Categorical and Interval Targets10 min
Splitting Criteria in Model Studio10 min
Adjustments in a Split Search10 min
Missing Values in Decision Trees in Model Studio10 min
Surrogate Splits10 min
Calculating Variable Importance for Surrogate Splits10 min
Bottom-Up Pruning Requirements10 min
Pruning Options in Model Studio10 min
Autotuning Options for Decision Trees in Model Studio10 min
Gradient Boosting Models10 min
Autotuning Options for Gradient Boosting in Model Studio10 min
Autotuning Options for Forests in Model Studio10 min
11 exercices pour s'entraîner
Question 3.01
Question 3.0230 min
Question 3.0330 min
Question 3.0430 min
Question 3.0530 min
Think About It30 min
Question 3.0630 min
Question 3.0730 min
Question 3.08
Question 3.0930 min
Decision Trees and Ensembles of Trees Quiz30 min
8 heures pour terminer
Neural Networks
In this module, you learn to build neural network models.
8 heures pour terminer
18 vidéos (Total 37 min), 10 lectures, 13 quiz
18 vidéos
Beyond Traditional Regression: Neural Networks3 min
Limitations of Neural Networks2 min
Basics of Neural Networks3 min
Estimating Weights and Making Predictions3 min
Learning Process2 min
Essential Discovery Tasks for Neural Networks24s
Demo: Building a Neural Network Using the Default Settings3 min
Improving the Neural Network Model22s
Neural Network Architectures4 min
Activation Functions1 min
Shaping the Sigmoid2 min
Demo: Modifying the Neural Network Architecture1 min
Optimizing the Complexity of a Neural Network Model40s
Weight Decay1 min
Early Stopping2 min
Regularizing and Tuning the Hyperparameters of a Neural Network Model32s
Demo: Modifying the Learning and Optimization Parameters2 min
10 lectures
Standardization Methods10 min
Iterative Updating in Numerical Optimization10 min
Numerical Optimization Methods in Model Studio10 min
Deviance Measures in Model Studio10 min
Calculating the Number of Parameters10 min
Deep Learning10 min
Hidden Layer Activation Functions in Model Studio10 min
Target Layer Activation Functions and Error Functions in Model Studio10 min
Selected Hyperparameters Related to the Learning Process in Model Studio10 min
Autotuning Options for Neural Networks in Model Studio10 min
8 exercices pour s'entraîner
Question 4.0130 min
Question 4.0230 min
Question 4.0330 min
Question 4.0430 min
Question 4.0530 min
Question 4.0630 min
Question 4.0730 min
Neural Networks Quiz30 min
Avis
Meilleurs avis pour APPRENTISSAGE AUTOMATIQUE À L'AIDE DE SAS VIYA
par EO5 janv. 2021
The content is great, although the platform failed after 5 days of inactivity. My account was deactivated and I was never able to recover it so I couldn't follow the tutorials.
par TA5 juil. 2019
Easy to follow even with limited statistics knowledge. Instructors teach the basics to get you started and best of all the software is included so you can practice.
par RM7 avr. 2020
I found this course SAS Viya really excellent and useful for Data Scientist. I would recommend it to anyone interested in learning and deploy machine learning.
par KK11 mai 2020
nice course for those who already knew machine learning concepts and wants to learn SAS viya for creating models.
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