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Artificial Intelligence and Machine Learning Basics for Non-Technical Professionals

Class Sessions

Date Location Cost
  • Thu Jun 11, 9:00 am to 4:00 pm
Live Online $400
  • Wed Jul 8, 9:00 am to 4:00 pm
Live Online $400
  • Mon Aug 10, 9:00 am to 4:00 pm
Live Online $400

Class Code

ITS-1918

Class Description

Effective immediately in response to COVID-19, all Technology Training classes will be delivered online until further notice.


In advance of each session, Tech Training will provide you with a Zoom link to your class, along with any required class materials.
 



This course is a fun and non-technical introduction to Artificial Intelligence and Machine Learning. Get acquainted with the vocabulary and basics of this exciting new world.


Prerequisite: Basic programming knowledge preferred

 

This Artificial Intelligence (AI) and Machine Learning (ML) class helps increase awareness about AI and ML patterns and use cases in the real world. You will get an understanding of ML concepts like Supervised and Unsupervised learning techniques and usages. We will discuss the difference between AI vs ML vs Deep Learning (DL) along with usage patterns. Expand your vocabulary in AI to understand techniques like Classification, Clustering, and Regression. Finally, there will be a ML demo to illustrate a few tools and next steps.

 

In this course, you will have an opportunity to learn how to:

  • Describe Supervised and Unsupervised learning techniques and usages
  • Compare AI vs ML vs DL
  • Understand techniques like Classification, Clustering, and Regression
  • Discuss how to identify which techniques to apply for specific use cases
  • Understand the popular Machine offerings like Amazon Machine Learning, TensorFlow, Azure Machine Learning, Spark mlib, Pythonand R etc.
  • Understand the relation 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
  • Define how to form a ML team for success
  • Understand usage of tools through a ML Demo and hands-on labs.

 

Topic Outline:

  • Course Introduction
  • History and background of AI and ML
  • Compare AI vs ML vs DL
  • Describe Supervised and Unsupervised learning techniques and usages
  • Machine Learning patterns
    - Classification
    - Clustering
    - Regression
  • Gartner Hype Cycle for emerging technologies
  • Machine Learning offerings in the industry
  • Discuss Machine Learning use cases in different domains
  • Understand the Data Science process to apply to ML use cases
  • Understand the relation between Data Engineering and Data Science
  • Identify the different roles needed for successful ML project
  • Hands-on: Create an account for Microsoft Azure Machine Learning Studio
  • Demo: ML using Azure ML studio
  • Demo: ML using Scikit-learn
  • References and next steps

 



 

University IT Technology Training classes are only available to Stanford University staff, faculty, or students. A valid SUNet ID is needed in order to enroll in a class.