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    Using SAS Viya REST APIs with Python and R

    BY
    SAS Institute via Coursera

    Brochure Enquire
    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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