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.
In addition, some courses are available as live, instructor-led training from one of our partners.
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
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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.
