SAS AI and Machine Learning Professional Subscription

Designed for data scientists who want to learn to apply AI and machine learning to business problems and understand each step of the analytical life cycle.
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Digital courses
Unlimited access to digital courses and books in this subscription.
Prepare for certification
Unlimited access to certification preparation materials.
Shareable digital badges
Earn a digital badge for each completed course.
Hands-on learning and practice
Get Full access to SAS software to practice as you learn.

About this Subscription

The AI and Machine Learning Professional program includes five artificial intelligence and machine learning courses. The paid learning subscription also includes 70 hours of cloud software access to practice and the ability to earn five digital course badges in a 12-month license. You will learn essential data science skills, including machine learning, natural language and computer vision, and forecasting and optimization.

Learn how to:

  • Apply the analytical life cycle to business need.
  • Preprocess data and perform feature engineering.
  • Create supervised machine learning models and assess model performance.
  • Deploy models into production.
  • Analyze text and image data to recognize patterns.
  • Define and understand deep learning and build models using computer vision techniques.
  • Use pipelines to generate forecast and select champion pipelines.
  • Identify and solve optimization problems.

It is recommended that you have prior programming experience using SAS, Python or R. You can gain programming knowledge in the SAS Programming 1: Essentials course. Candidates should also have some experience with the visual interfaces on the SAS Viya. platform You can learn about those interfaces in the course SAS Visual Statistics in SAS Viya: Interactive Model Building or SAS Visual Analytics 1 for SAS Viya: Basics. It is helpful to have a conceptual understanding of regression models, which you can gain by completing the Statistics 1: Introduction to ANOVA, Regression, and Logistic Regression course. It is also helpful to have a conceptual understanding of neural network models, which you can gain by completing the course Neural Networks: Essentials or Neural Network Modeling.

Courses Included

SAS Machine Learning Specialist Certification

1

  • COURSE

    Machine Learning Using SAS® Viya®
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    This course discusses the theoretical foundation for techniques associated with supervised machine learning models. A series of demonstrations and practices is used to reinforce all the concepts and the analytical approach to solving business problems. 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, by illustrating data exploration, data preprocessing, feature selection, model training and validation, model assessment, and scoring. This course is the core of the SAS Viya Data Mining and Machine Learning curriculum. It 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.

  • CERTIFICATION PREP

    SAS Certified Specialist: Machine Learning Using SAS Viya
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    In preparation for the SAS Viya Supervised Machine Learning Pipelines certification exam, you will learn use SAS Visual Data Mining and Machine Learning software to prepare data and feature engineering; create supervised machine learning models; assess; model performance; and deploy models into production.

  • CERTIFICATION PREP

    Practice Exam: SAS Viya Supervised Machine Learning Pipelines
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    This Certification is for data scientists who create supervised machine learning models using pipelines in SAS Viya. Successful candidates should be familiar with SAS Visual Data Mining and Machine Learning software and be skilled in tasks such as:

    • Preparing data and feature engineering
    • Creating supervised machine learning models
    • Assessing model performance
    • Deploying models into production

    Enroll

SAS Natural Language Processing and Computer Vision Specialist Certification

2

  • COURSE

    SAS® Visual Text Analytics in SAS® Viya®
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    SAS Visual Text Analytics enables you to uncover insights hidden within unstructured data using the combined power of natural language processing, machine learning, and linguistic rules. This course explores the five components of Visual Text Analytics: parsing, concept derivation, topic derivation, text categorization, and sentiment analysis. Documents are parsed and analyzed to reveal dominant themes in the document collection. Sophisticated linguistic queries are constructed to satisfy specific information needs. An integrated solution is developed using information extracted from subject matter expert rules, combined with machine learning results for model and rule-based topics and categories. The course includes hands-on use of SAS Viya in a distributed computing environment.

  • COURSE

    Deep Learning Using SAS® Software
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    This course introduces the pivotal components of deep learning. You learn how to build deep feedforward, convolutional, and recurrent networks. Neural networks are used to solve problems that include traditional classification, image classification, and sequence-dependent outcomes. The course contains a healthy mix of theory and application. Hands-on demonstration and practice problems are included to reinforce key concepts. Hyperparameter search methods are described and demonstrated to find an optimal set of deep learning models. Lastly, transfer learning is covered because the emergence of this field has shown promise in deep learning.

  • CERTIFICATION PREP

    SAS Certified Specialist: Natural Language Processing and Computer Vision Using SAS Viya
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    In preparation for this exam, you will learn how to leverage the natural language processing and computer vision functionality of Model Studio in SAS Viya including loading and exploring data, identifying text patterns using both natural language processing and computer vision techniques.

  • CERTIFICATION PREP

    Practice Exam: Natural Language Processing and Computer Vision Specialist
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    This Certification is for data scientists, text and image analysts, AI specialists and others who analyze text and image data to recognize patterns. Successful candidates are familiar with SAS Model Studio for SAS Viya, SAS Visual Text Analytics, and SAS Data Mining and Machine Learning. They are skilled in tasks such as:

    • Text topic detection
    • Sentiment analysis
    • Image processing
    • Image classification
    • Deep learning with static and dynamic data



SAS Forecasting and Optimization Specialist Certification

3

  • COURSE

    Forecasting Using Model Studio in SAS® Viya®
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    This course provides a hands-on tour of the forecasting functionality in Model Studio, a component of SAS Viya. The course begins by showing how to load the data into memory and visualize the time series data to be modeled. Attribute variables are introduced and implemented in the visualization. The course then covers the essentials of using pipelines for generating forecasts and selecting champion pipelines in a project. It also teaches you how to incorporate large-scale forecasting practices into the forecasting project. These include the creation of data hierarchies, forecast reconciliation, overrides, and best practices associated with forecast model selection.

  • COURSE

    Optimization Concepts for Data Science and Artificial Intelligence
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    This course focuses on linear, nonlinear, and mixed integer linear optimization concepts in SAS Viya. Students learn how to formulate optimization problems and how to make their formulations efficient by using index sets and arrays. The demonstrations in the course include examples of diet formulation and portfolio optimization. Learn the OPTMODEL procedure and open-source tools to formulate and solve optimization problems.

  • CERTIFICATION PREP

    SAS Certified Specialist: Forecasting and Optimization Using SAS Viya
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    In preparation for the SAS Viya Forecasting and Optimization certification exam, you will learn how to leverage the forecasting functionality of Model Studio in SAS Viya including data visualization, pipeline modeling, hierarchical forecasting, and post-forecasting functions. You will also learn how to formulate and solve linear, nonlinear, and mixed integer linear optimization problems using the OPTMODEL procedure in SAS Viya.

  • CERTIFICATION PREP

    Practice Exam: Forecasting and Optimization Using SAS Viya
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    Practice Exam: Forecasting and Optimization Using SAS Viya

SAS ModelOps Specialist Certification

4

  • COURSE

    ModelOps: Governing AI and Machine Learning Models That Drive Your Business with SAS®
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    Companies need to continually adapt to changing business conditions. They can make informed decisions with the help of predictive models, but many businesses still struggle with getting models into production in a reasonable timeframe. This course describes how awareness of ModelOps (model operations) can streamline the journey from preparing data, building models, and deploying them in production environments to managing their performance. Proven DevOps (development operations) disciplines are evolving into the unique practice of deploying models in today’s environment.
    Use of SAS Container Runtime and SAS Model Risk Management are also addressed, as are pointers about administrating a production environment.
    This course helps candidates prepare for the SAS Certified ModelOps Specialist exam along with the topics covered in the Managing Models in SAS Viya course.

  • COURSE

    Managing Models in SAS® Viya®
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    This applied, hands-on course teaches you how to manage models through their useful life cycle. After creating a modeling project, you add and compare models to it so that you can identify a champion model. The course uses models that are created using SAS Advanced Analytics capabilities, Python, and R. The course also shows how to implement workflow to ensure that model governance and oversight approval is being followed.

    You learn how to test a model in the production environment in which it will be deployed. After the model test completes successfully, you learn how to schedule the model so it can run automatically.

    Further, the course shows how to measure and monitor the ongoing model performance over time. The performance monitoring process will also be scheduled to run automatically in class.

    An optional lesson shows how to register and score SAS Visual Text Analytics models.

  • CERTIFICATION PREP

    SAS Certified ModelOps Specialist
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    In preparation for this exam, you will learn how to define the ModelOps framework, develop model proposals, develop and manage models, and deploy models. You will also learn how to execute multiple production steps including preprocessing, scoring, post-processing, business actions, and performance monitoring.

  • CERTIFICATION PREP

    Practice Exam: Managing the Model Life Cycle Using ModelOps
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    Practice Exam: Managing the Model Life Cycle Using ModelOps

Advanced and Specialty Topics

5

  • COURSE

    Advanced Machine Learning Using SAS® Viya®
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    This course teaches you how to optimize the performance of predictive models beyond the basics by implementing various data munging and wrangling techniques. The course continues the development of supervised learning models that begins in the Machine Learning Using SAS Viya course and extends it to ensemble modeling. Running unsupervised learning and semi-supervised learning models is also discussed. In this course, you learn how to do feature engineering and clustering of variables, and how to preprocess nominal variables and detect anomalies. This course uses Model Studio, the pipeline flow interface in SAS Viya that enables you to prepare, develop, compare, and deploy advanced analytics models. Importing and running external models in Model Studio is also discussed, including open-source models. SAS Viya automation capabilities at each level of machine learning are also demonstrated, followed by some tips and tricks with Model Studio.

  • COURSE

    Using Python and R with SAS® Viya® for Advanced Analytics
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    In this course, you learn to use R and Python to take control of the SAS Viya Cloud Analytic Services (CAS) distributed computing environment to develop machine learning models. You learn to upload data into the in-memory distributed environment, analyze data using Pandas like functionality, build machine learning models, and assess those models in CAS using familiar open-source functionality via the SWAT (SAS Scripting Wrapper for Analytics Transfer) package.

  • COURSE

    Advanced Analytics for IoT Using SAS® Event Stream Processing
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    This course teaches you how to apply advanced analytics techniques to IoT processes. The course addresses analysis of data at rest as well as streaming data. By using the SAS Viya environment with SAS Event Stream Processing, you learn how to deploy your own deep learning models to streaming data.

  • COURSE

    Network Analysis and Network Optimization in SAS® Viya®
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    This course provides a set of network analysis and network optimization solutions using the NETWORK and OPTNETWORK procedures in SAS Viya. Real-world applications are emphasized for each algorithm introduced in this course, including using network analysis as a stand-alone unsupervised learning technique, as well as incorporating network analysis and optimization to augment supervised learning techniques to improve machine learning model performance through input/feature creation.

  • COURSE

    Tree-Based Machine Learning Methods in SAS® Viya®
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    Decision trees and tree-based ensembles are supervised learning models used for problems involving classification and regression. This course covers everything from using a single tree to more advanced bagging and boosting ensemble methods in SAS Viya. The course includes discussions of tree-structured predictive models and the methodology for growing, pruning, and assessing decision trees, forest models, and gradient boosting models. The course also explains isolation forest (an unsupervised learning algorithm for anomaly detection), deep forest (an alternative for neural network deep learning), and Poisson and Tweedie gradient boosted regression trees. In addition, many of the auxiliary uses of trees, such as exploratory data analysis, dimension reduction, and missing value imputation, are examined, and running open source in SAS and running SAS in open source are demonstrated for tree-based ensemble models.

  • COURSE

    Neural Networks: Essentials
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    This course combines theory and practice to immerse you in the core concepts of neural network models and the essential practices of real-world application. During the course, you programmatically build a neural network and discover how to adjust the model’s essential parameters to solve different types of business challenges. You implement early stopping, build autoencoders for a predictive model, and perform an intelligent automatic search of the model hyperparameter values. The last lesson introduces deep learning. You gain hands-on practice building neural networks in SAS 9.4 and the cutting-edge, cloud-enabled in-memory analytics engine for big data analytics, SAS Viya.

AI Literacy

6

  • COURSE

    Data Literacy Essentials
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    This course is for anyone interested in learning how to use data in meaningful ways. It is designed as an introductory course to data literacy, starting with the basics: what is data, what does it mean to be data literate, and why is it important in today’s world?

    This self-paced course follows the journeys of a concerned parent, a small business owner, and a public health expert, all of whom rely on data to navigate the COVID-19 pandemic. It connects skills we already use to strategies for engaging with data in more intentional and meaningful ways.

    Whether you are an advanced high school student, a K-12 educator, a post-secondary student or educator, or an independent learner hoping to reskill or upskill, this course is for you.

  • COURSE

    Data Literacy in Practice
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    In this self-paced course, you will discover practical skills to explore and visualize data. You will follow a small business owner's data-driven journey to improve company performance. Using this real-world context, you'll connect knowledge to strategies you can act on. The course is for everyone, no matter where you are on your data literacy journey. By focusing on conceptual and practical understanding rather than distracting mathematical jargon, learners feel supported and encouraged throughout the process. Whether you are a high school student, a K-12 educator, a post-secondary student or educator, or an independent learner hoping to reskill or upskill, this course is for you.

  • COURSE

    Generative AI Using SAS®
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    Generative Artificial Intelligence (GenAI) is a rapidly developing area of machine learning, with application across business, government, and academia. In this course, you will learn about different types of GenAI and see examples of how SAS can enhance your efforts to make the most of these techniques.

    New lessons will be added to this course periodically as this field is rapidly developing. We expect that each lesson can be completed in about an hour, and you can work at your own pace to complete the material. As we release new lessons, you might lose progress through the material that you have completed, so please make a note of where you are leaving off before exiting the course.

  • COURSE

    Responsible Innovation and Trustworthy AI
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    This course is designed for anyone who wants to gain a deeper understanding about the importance of trust and responsibility in AI, analytics, and innovation. The content is especially geared to those who are making business decisions based on machine learning and AI systems and those who are designing and training AI systems.

    Whether you are a programmer, an executive, an advisory board member, a tester, a manager, or an individual contributor, this course helps you gain foundational knowledge and skills to consider the issues related to responsible innovation and trustworthy AI. Empowered with the knowledge from this course, you can strive to find ways to design, develop, and use machine learning and AI systems more responsibly.

    We expect that each module can be completed in under an hour, and you can work at your own pace to complete the material. As we release new modules, you might lose progress through the material that you have completed, so please make a note of where you are leaving off before exiting the course.

Knowledge Badge

7

  • KNOWLEDGE BADGE

    AI Foundations Knowledge Badge
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    To earn the AI Foundations Knowledge Badge, you must pass an open-book multiple-choice assessment covering the importance of trustworthy and responsible practices in AI, analytics, and innovation.You will display foundational knowledge and skills to consider issues related to responsible innovation and trustworthy AI, including identifying unwanted biases, applying principles of responsible innovation, and understanding how SAS technologies address these issues. You will show your understanding of the foundational knowledge of Generative AI and its capabilities, limitations, applications and how SAS tools can be leveraged to drive business value while addressing the risks and challenges of this transformative technology.The badge is intended for individuals using, designing, and monitoring AI systems.

SAS Products Covered

  • SAS Viya
  • SAS Visual Text Analytics
  • SAS Machine Learning
  • SAS Visual Forecasting
  • SAS Optimization
  • SAS Model Manager
  • SAS Visual Statistics
  • SAS Analytics Platform
  • SAS Event Stream Processing


Digital Badges

Earn badges as you complete courses, pass knowledge assessments or certification exams. Then showcase your achievements to colleagues potential employers, and your social networks.

Certification Preparation

When you complete the courses in this subscription, you will have the demonstrated skills necessary to prepare you to earn the following credentials. After earning all three credentials, you will be awarded the SAS Certified AI and Machine Learning Professional credential.

“I think the self-paced training was the BEST I’ve ever taken. The videos were short and segmented correctly to keep my attention and the activities and quizzes were just enough to help my confidence.”

Tony Mayo, SAS Customer