SAS Visual Data Mining and Machine Learning

標題 等級 培訓格式
FDP - Shaping an Analytics Curriculum (A SAS & Open Source Approach)
This outline is provisional and subject to change.The Faculty Development Program (FDP) teaches you fundamental concepts and relevant techniques in analytics and machine learning using a powerful mix of SAS and open-source programming languages. The course also enables you to explore academic and collaborative opportunities with SAS in the area of advance analytics for designing better curriculum and effective pedagogy.

0 No level Live Web Classroom
SAS Viya and Python Integration for Machine Learning New
本課程介紹如何在Jupyter Notebook 上利用Python 程式進行CAS 的操作,包含透過SWAT 套件上傳資料到分散式記憶體環境、分析資料、建立預測模型,以及把結果下載到本機端利用Python 原生語法進行模型比較。

3 Intermediate Classroom Live Web Classroom
SAS Viya and Python Integration for Machine Learning
本課程介紹如何在JupyterNotebook 上利用Python 程式進行CAS 的操作,包含透過SWAT 套件上傳資料到分散式記憶體環境、分析資料、建立預測模型,以及把結果下載到本機端利用Python 原生語法進行模型比較。

3 Intermediate Classroom Live Web Classroom
Using SAS Viya REST APIs with Python and R
In this course, you learn to use the R and Python APIs to take control of SAS Cloud Analytic Services (CAS) and submit actions from Jupyter Notebook. You learn to upload data into the in-memory distributed environment, analyze data, and create predictive models on CAS using familiar open-source functionality via the SWAT (SAS Wrapper for Analytics Transfer) package.

3 Intermediate e-Learning
Deep Learning Using SAS Software
本課程為深度學習的基礎課程,深度神經網路常用在分類問題、影像辨識或時間序列等問題上。課程中將介紹如何建立深度前饋神經網路,卷積神經網路以及遞迴神經網路;同時也會提到如何增強訓練資料集以提高模型效果的實用方法。在課程的最後,則會介紹如何有效率的找到適合的超參數設定值。

3 Intermediate Classroom Live Web Classroom e-Learning
Tree-Based Machine Learning Methods in SAS Viya
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 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 Tweedy 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.

The self-study e-learning includes:

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

4 Expert e-Learning
Advanced Machine Learning Using SAS Viya
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 are 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.

4 Expert e-Learning