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- Using SAS Viya REST APIs with Python and R
Using SAS Viya REST APIs with Python and R
Quick Facts
| particular | details | ||||
|---|---|---|---|---|---|
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Medium of instructions
English
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Mode of learning
Self study
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Mode of Delivery
Video and Text Based
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Learning efforts
5 Hours Per Week
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Course and certificate fees
Type of course
Free
certificate availability
Yes
certificate providing authority
Coursera
certificate fees
₹2,421
The syllabus
Course Overview
Videos
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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