SAS Machine Learning Specialist Using SAS Viya Course in Pune

Data preparation capabilities

Algorithms – basic and advanced

Automation and iterative processes

Scalability

Ensemble modelling

Supervised learning

Unsupervised learning

Semi-supervised learning

Reinforcement learning

All about machine learning algorithms

Boost your SAS skills

Will machine learning change your organization?

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Manufacturing

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P-12 Education

Public Sector

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Small & Midsize Business

Sports Analytics

Travel & Transportation

Telecom, Media & Technology

Utilities

1. SAS Programming 1: Essentials

2. SAS Programming 2: Data Manipulation Techniques

3. Accelerating SAS Code on the SAS Viya Platform

4. Statistics You Need to Know for Machine Learning

5. Machine Learning Using SAS Viya

Apply the analytical life cycle to a business need.

Incorporate a business-problem-solving approach in daily activities.

Prepare and explore data for analytical model development

Create and select features for predictive modeling.

Develop a series of supervised learning models based on different techniques such as decision trees, ensembles of trees (forest and gradient boosting), neural networks, and support vector machines.

Evaluate and select the best model based on business needs.

Deploy and manage analytical models under production.

It proves your high level of expertise

You are recognized as an SAS Machine Learning Specialist in the industry

Your SAS certification is globally acknowledged

Demonstrate your true SAS skills and understanding

You can learn as you earn

SAS Certification Allow employers to easily discover you

SAS Machine Learning Specialist Certification Teaches you the basics of data mining

You get an official digital badge (Learning and Certified)

Your SAS certification will never expire

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Demonstrate your true SAS skills and understanding

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Receive ongoing support and be a part of the community

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SAS Certified Trainers with Instructor LED Online Training (Demonstrate your true SAS skills and understanding.)

SAS Official Curriculum, mapped to globally recognized credentials.

Hands on learning (Learn SAS Data Mining, Machine Learning and Deep learning Course with a SAS Certified Expert in a traditional classroom setting for an in-depth, hands-on learning experience.)

Receive ongoing support and be a part of a community.

Software Access (Access the latest software 24*7 on Azure servers)

Earn a Globally Recognized Digital Badge (A secure, electronic verification of your achievement. Allow employers to easily discover you)

SAS Global Certification Credentials (Earn your Machine Learning Specialist Using SAS Viya credentials and be recognized across all industries and geographic locations).

▪ navigate the SAS windowing environment

▪ read various types of data into SAS data sets

▪ navigate the SAS Enterprise Guide programming environment

▪ create SAS variables and subset data

▪ combine SAS data sets

▪ create and enhance listing and summary reports

▪ validate SAS data sets.

▪ control SAS data set input and output

▪ combine SAS data sets

▪ summarize, read, and write different types of data

▪ perform DO loop and SAS array processing

▪ transform character, numeric, and date variables

▪ Run traditional SAS code on the SAS Viya platform.

▪ Connect to SAS Cloud Analytic Services (CAS).

▪ Access and use caslibs on the CAS server.

▪ Load SAS data sets, Parquet files, CSV files, Microsoft Excel files, and DBMS tables into CAS

▪ performing simple tests of hypothesis

▪ Modify DATA step code to execute in CAS.

▪ Modify SQL procedure code to execute in CAS using FedSQL.

▪ Modify SQL procedure code to execute in CAS using FedSQL.

▪ Write CASL code to execute actions on the CAS server.

▪ Explain the relevance of statistics in big data and machine learning.

▪ Relate statistical and data science terminology

▪ Generate descriptive statistics and explore data with graphs.

▪ Detect associations among variables.

▪ Perform linear regression for explanatory modeling.

▪ Compare explanatory modeling with predictive modeling.

▪ Describe the trade-off between bias and variance.

▪ Fit a logistic regression model for predictive modeling.

▪ Score new data.

▪ Explain the statistical foundations of machine learning.

▪ Discuss data difficulties and modeling issues and their statistical solutions

▪ Apply the analytical life cycle to a business need.

▪ Incorporate a business-problem-solving approach in daily activities.

▪ Prepare and explore data for analytical model development.

▪ Create and select features for predictive modeling.

▪ Develop a series of supervised learning models based on different techniques such as decision trees, ensembles of trees (forest and gradient boosting), neural networks, and support vector machines.

▪ Evaluate and select the best model based on business needs.

▪ Deploy and manage analytical models under production