Course Number: PYTH-206

Duration: 1 day (6.5 hours)

Format: Live, hands-on

AI and ML for Executives Training Overview

This live, online Artificial Intelligence (AI) and Machine Learning (ML) Basics for Executives training course provides attendees with a non-technical introduction to AI and ML. Participants learn ML concepts, including supervised and unsupervised learning techniques and usages. This course explains the differences among AI, ML, and DL, along with usage patterns. Attendees expand their AI vocabulary to understand techniques like Classification, Clustering, and Regression.

Location and Pricing

This course is taught as a private, live online class for teams of 3 or more. All our courses are hands-on, instructor-led, and tailored to fit your group’s goals and needs. Most Accelebrate classes can be flexibly scheduled for your group, including delivery in half-day segments across a week or set of weeks. To receive a customized proposal and price quote for online corporate training, please contact us.

Objectives

  • Describe supervised and unsupervised learning techniques and usages
  • Compare AI versus ML versus DL
  • Understand techniques like classification, clustering, and regression
  • Identify which kinds of techniques should be applied for a specific use case
  • Understand the popular Machine offerings, including Amazon Machine Learning, TensorFlow, Azure Machine Learning, Spark mlib, Python, and R.
  • Understand the relationship between Data Engineering and Data Science
  • Understand the Data Science process
  • Discuss Machine Learning use cases in different domains
  • Identify when to use or not use Machine Learning
  • Understand how to form a successful ML team
  • Understand usage of tools through an ML Demo and hands-on labs

Prerequisites

Attendees should have basic Programming knowledge.

Outline

Expand All | Collapse All

Course Introduction
History and Background of AI and ML
Compare AI vs ML vs DL
Supervised and Unsupervised Learning Techniques and Usages
Machine Learning Patterns
  • Classification
  • Clustering
  • Regression
Gartner Hype Cycle for Emerging Technologies Machine Learning Offerings in Industry
Machine Learning Use Cases in Different Domains
The Data Science Process to Apply to ML Use Cases
Identify the Different Roles Needed for a Successful ML Project
References and Next Steps
Structured Activity/Exercises/Case Studies:
  • Create an account for Microsoft Azure Machine Learning Studio
  • ML using Azure ML Studio
  • Demo of ML using Scikit-learn
Conclusion

Training Materials:

All AI and ML training students receive comprehensive courseware.

Software Requirements:

Detailed setup will be provided upon request.



Learn faster

Our live, instructor-led lectures are far more effective than pre-recorded classes

Satisfaction guarantee

If your team is not 100% satisfied with your training, we do what's necessary to make it right

Learn online from anywhere

Whether you are at home or in the office, we make learning interactive and engaging

Multiple Payment Options

We accept check, ACH/EFT, major credit cards, and most purchase orders



Recent Training Locations

Alabama

Birmingham

Huntsville

Montgomery

Alaska

Anchorage

Arizona

Phoenix

Tucson

Arkansas

Fayetteville

Little Rock

California

Los Angeles

Oakland

Orange County

Sacramento

San Diego

San Francisco

San Jose

Colorado

Boulder

Colorado Springs

Denver

Connecticut

Hartford

DC

Washington

Florida

Fort Lauderdale

Jacksonville

Miami

Orlando

Tampa

Georgia

Atlanta

Augusta

Savannah

Hawaii

Honolulu

Idaho

Boise

Illinois

Chicago

Indiana

Indianapolis

Iowa

Cedar Rapids

Des Moines

Kansas

Wichita

Kentucky

Lexington

Louisville

Louisiana

New Orleans

Maine

Portland

Maryland

Annapolis

Baltimore

Frederick

Hagerstown

Massachusetts

Boston

Cambridge

Springfield

Michigan

Ann Arbor

Detroit

Grand Rapids

Minnesota

Minneapolis

Saint Paul

Mississippi

Jackson

Missouri

Kansas City

St. Louis

Nebraska

Lincoln

Omaha

Nevada

Las Vegas

Reno

New Jersey

Princeton

New Mexico

Albuquerque

New York

Albany

Buffalo

New York City

White Plains

North Carolina

Charlotte

Durham

Raleigh

Ohio

Akron

Canton

Cincinnati

Cleveland

Columbus

Dayton

Oklahoma

Oklahoma City

Tulsa

Oregon

Portland

Pennsylvania

Philadelphia

Pittsburgh

Rhode Island

Providence

South Carolina

Charleston

Columbia

Greenville

Tennessee

Knoxville

Memphis

Nashville

Texas

Austin

Dallas

El Paso

Houston

San Antonio

Utah

Salt Lake City

Virginia

Alexandria

Arlington

Norfolk

Richmond

Washington

Seattle

Tacoma

West Virginia

Charleston

Wisconsin

Madison

Milwaukee

Alberta

Calgary

Edmonton

British Columbia

Vancouver

Manitoba

Winnipeg

Nova Scotia

Halifax

Ontario

Ottawa

Toronto

Quebec

Montreal

Puerto Rico

San Juan