Using SAS Viya REST APIs with Python and R

BY
SAS Institute via Coursera

Mode

Online

Duration

3 Weeks

Fees

Free

Quick Facts

particular details
Medium of instructions English
Mode of learning Self study
Mode of Delivery Video and Text Based
Learning efforts 5 Hours Per Week

Course and certificate fees

Type of course

Free

certificate availability

Yes

certificate providing authority

Coursera

certificate fees

₹2,421

The syllabus

Course Overview

Videos
  • Course Overview
Readings
  • Learner Prerequisites
  • Using SAS® Viya® for Learners with This Course (Required)
  • Course Information (Required)
  • Using Forums and Getting Help

SAS® Viya® and Open Source Integration

Videos
  • SAS Approach to Open Source Integration
  • Cloud Analytic Services
  • Jupyter Notebooks and Open Source Development Interfaces
  • SAS Scripting Wrapper for Analytics Transfer
  • CAS Actions in SAS Viya
  • Connecting to CAS and Reading in Data
  • DataFrames and CAS Tables on the Clients and Server
  • Advantages to Open Source Integration
  • Demo: Getting Started with CAS and the R API
  • Demo: Getting Started with CAS and the Python API
Practice Exercise
  • SAS® Viya® and Open Source Integration Quiz
  • Question 2.01
  • Question 2.02
  • Question 2.03
  • Question 2.04

Machine Learning

Videos
  • Introduction to Predictive Modeling
  • Data Partitioning: Preventing Overfitting
  • Logistic Regression Models
  • Support Vector Machines
  • Decision Trees
  • Ensemble of Trees
  • Neural Network Models
  • Autotuning Hyperparameters
  • Model Performance Assessment
  • Model Performance Charts: ROC and Lift
  • Demo: Using the R API to Create and Assess Models
  • Demo: Using the Python API to Create and Assess Models
  • Demo: Creating a Gradient Boosting Model in SAS Studio
  • Demo: Using R Functions and Looping for Efficient Coding
  • Demo: Using Python Functions and Looping for Efficient Coding
Practice Exercise
  • Machine Learning Quiz
  • Question 3.01
  • Question 3.02
  • Question 3.03

Text Analytics

Videos
  • Text Analytics
  • Natural and Formal Languages
  • Processing Words
  • Processing Context
  • Processing Concepts
  • Extracting Information from the Term-Document Matrix
  • Word Embedding
  • Demo: Using the R API to Explore Text Documents
  • Demo: Using the Python API to Explore Text Documents
Practice Exercise
  • Text Analytics Quiz
  • Question 4.01
  • Question 4.02

Deep Learning

Videos
  • Traditional Neural Networks
  • Hidden Unit Activation Functions
  • Weight Initialization
  • Regularization Methods
  • Nonlinear Optimization Algorithms (or Gradient-Based Learning)
  • Processors for Analytics
  • Deep Neural Networks (DNN) versus Recurrent Neural Networks (RNN)
  • Recurrent Neural Network Architecture
  • Improving RNN Models
  • Gated Recurrent Unit (GRU)
  • Long Short-Term Memory (LSTM)
  • Demo: Deep Learning Sentiment Prediction Using the R API
  • Demo: Deep Learning Sentiment Prediction Using the Python API
Practice Exercise
  • Deep Learning Quiz
  • Question 5.01
  • Question 5.02

Time Series

Videos
  • Time Series Forecasting
  • Model Performance and Assessment
  • Weighted Averages
  • Simple Exponential Smoothing
  • ARIMAX Models and Stationarity
  • Autoregressive and Moving Average Terms
  • Forecasting with Recurrent Neural Networks
  • Demo: Automatic Forecasting Using the R API
  • Demo: Automatic Forecasting Using the Python API
  • Demo: Deep Learning Forecasting Using the R API
  • Demo: Deep Learning Forecasting Using the Python API
Practice Exercise
  • Time Series Quiz
  • Question 6.01
  • Question 6.02
  • Question 6.03

Image Classification

Videos
  • Image Classification and Object Detection
  • Convolutional Neural Networks for Image Classification
  • Convolution Layers
  • Pooling Layers
  • Fully Connected and Output Layers
  • Demo: Classifying Color Images Using the R API
  • Demo: Classifying Color Images Using the Python API
Practice Exercise
  • Image Classification Quiz
  • Question 7.01

Factorization Machines

Videos
  • Recommender Systems
  • Factorization Machines for Recommendation
  • Demo: Modeling Sparse Data Using the R API
  • Demo: Modeling Sparse Data Using the Python API
Practice Exercise
  • Factorization Machines Quiz
  • Question 8.01

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