SAS Advanced Machine Learning Subscription

For machine learning modelers who are well versed in machine learning basics and want to go deeper. These advanced machine learning courses address many different types of models and their applications in artificial intelligence.
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About this Subscription


This learning subscription includes advanced machine learning courses that teach how to train and customize production-ready machine learning models using many different techniques, including deep learning, optimization, supervised and semi-supervised models, post-fitting model assessment, and more. Advanced machine learning uses patterns from observed data and uses those patterns to predict future results or unobservable data. You’ll learn how to do feature engineering, automate different stages of machine learning, leverage deep learning, optimize networks, benefit from ensembles of models such as trees, and use a ModelOps approach to manage the model deployment process.

Learn how to:

  • Optimize the performance of machine learning models by implementing various data preprocessing and post-fitting techniques.
  • Automate different stages of machine learning in SAS Viya.
  • Build models using deep learning techniques and use neural networks to solve problems that include traditional classification, image classification, and sequence-dependent outcomes.
  • Incorporate network analysis and network optimization solutions using the NETWORK and OPTNETWORK procedures in SAS Viya.
  • Analyze streaming data for IoT.
  • Use a ModelOps approach to govern and administer model deployment.
  • Before taking these courses, it is recommended that you take the SAS Programming 1: Essentials and Statistics 1: Introduction to ANOVA, Regression, and Logistic Regression courses, which are available in instructor-led or free online e-learning formats. The Statistics 2: ANOVA and Regression course or equivalent knowledge is also helpful.




    Courses Included in Subscription

    Advanced Modeling Techniques

    1
    • 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 Supervised Machine Learning Pipelines Using SAS(R) Viya(R) 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.

      The self-study e-learning includes:

      • Annotatable course notes in PDF format.
      • Virtual lab time to practice.


    • 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.

    • 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.

      The self-study e-learning includes:

      • Annotatable course notes in PDF format.
      • Virtual lab time to practice.


    • 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

      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.

      The self-study e-learning includes:

      • Annotatable course notes in PDF format.
      • Virtual lab time to practice.


    • 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.

    SAS Products Covered

    • SAS Visual Statistics
    • SAS Visual Data Mining and Machine Learning
    • SAS Viya
    • SAS Analytics Platform
    • SAS Event Stream Processing
    • SAS Model Manager


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    Certification Preparation

    This learning subscription is not currently associated with a SAS 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