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Course Description

Intro to AI Applications 1 Hour Course Description & Objectives:

  •  This course is asynchronous, meaning you can sign up any time throughout the year.
  •  This course aims to provide an iterative framework to develop real-world machine learning systems that learn from data, reason with data, are deployed, reliable and scalable. The focus of this course is to introduce basic modules of machine learning systems, namely, data management, data engineering, feature engineering, approaches to model selection, training, scaling, how to continually monitor and deploy changes to ML systems, as well as the human side of ML projects such as team structure and business metrics.
    •  By the end of this course, students will be able to:
      •  Define basic terminology for Artificial Intelligence (AI), Machine Learning (ML) and Deep Learning (DL) tools.
      •  Identify basic principles of data collection.
      •  Identify basic principles of data and feature engineering.
      •  Select the correct model for a particular task or application.

Fundamentals of AI Applications 4 Hour Course Description & Objectives:

  •  This course is asynchronous, meaning you can sign up any time throughout the year.
  • Upon completion, 0.4 CEUs are awarded.
  •  This course aims to provide an iterative framework to develop real-world machine learning systems that learn from data, reason with data, are deployed, reliable and scalable. The focus of this course is to introduce basic modules of machine learning systems, namely, data management, data engineering, feature engineering, approaches to model selection, training, scaling, how to continually monitor and deploy changes to ML systems, as well as the human side of ML projects such as team structure and business metrics.
    •  By the end of this course, students will be able to:
      •  Define basic terminology for Artificial Intelligence (AI), Machine Learning (ML) and Deep Learning (DL) tools.
      •  Apply appropriate principles of data collection.
      •  Identify basic principles of data and feature engineering.
      •  Create a suitable working environment.
      •  Select the correct model for a particular task or application.
      •  Identify meaningful experiments to evaluate the performance of ML models.

Notes

A badge or certificate is not awarded for the 1 hour course. 
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Enroll Now - Select a section to enroll in
Section Title
Intro to AI Applications- 1 hour
Type
self-paced
Dates
Start Now, you have 60 days to complete this course once enrolled.
Course Fee(s)
1 hour non-credit $0.00
Drop Request Deadline
TBD
Transfer Request Deadline
TBD
Section Notes
Description & Objectives:
  •  This course is asynchronous, meaning you can sign up any time throughout the year.
  •  This course aims to provide an iterative framework to develop real-world machine learning systems that learn from data, reason with data, are deployed, reliable and scalable. The focus of this course is to introduce basic modules of machine learning systems, namely, data management, data engineering, feature engineering, approaches to model selection, training, scaling, how to continually monitor and deploy changes to ML systems, as well as the human side of ML projects such as team structure and business metrics.
    •  By the end of this course, students will be able to:
      •  Define basic terminology for Artificial Intelligence (AI), Machine Learning (ML) and Deep Learning (DL) tools.
      •  Identify basic principles of data collection.
      •  Identify basic principles of data and feature engineering.
      •  Select the correct model for a particular task or application.
Section Title
Fundamentals of AI Applications- 4 hour
Type
self-paced
Dates
Start Now, you have 60 days to complete this course once enrolled.
Course Fee(s)
Registration fee non-credit $149.00
Drop Request Deadline
TBD
Transfer Request Deadline
TBD
Section Notes
Description & Objectives:
  •  This course is asynchronous, meaning you can sign up any time throughout the year.
  •  This course aims to provide an iterative framework to develop real-world machine learning systems that learn from data, reason with data, are deployed, reliable and scalable. The focus of this course is to introduce basic modules of machine learning systems, namely, data management, data engineering, feature engineering, approaches to model selection, training, scaling, how to continually monitor and deploy changes to ML systems, as well as the human side of ML projects such as team structure and business metrics.
    •  By the end of this course, students will be able to:
      •  Define basic terminology for Artificial Intelligence (AI), Machine Learning (ML) and Deep Learning (DL) tools.
      •  Apply appropriate principles of data collection.
      •  Identify basic principles of data and feature engineering.
      •  Create a suitable working environment.
      •  Select the correct model for a particular task or application.
      •  Identify meaningful experiments to evaluate the performance of ML models.
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