Artificial Intelligence
Engineering
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.
The AI Engineering package is made up of the three courses listed below. Click the register button to sign up for the 1-hour, 4-hour or 15-hour course.
If you’re interested in learning more about how to receive a micro-credential, click here.

Focus Area
Engineering
There Are 3 Courses Available in This Package
1-hr
Course
Intro to AI Applications
FREE Asynchronous
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.
4-hr
Course
Fundamentals of AI Applications
249 Asynchronous Course Completion Certificate 0.4 CEUs
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.
15-hr
Course
AI Applications
1095 HybridBadge Qualify for Micro-Credential 1.5 CEUs
This course is hybrid, meaning it will include a mixture of online work and live webinars.
The goal of this course is to present a set of modular applications of AI. This course will focus on several AI application areas, namely, AI in agriculture, AI in environmental systems, AI in healthcare, AI is cybersecurity, AI in defense, AI in IoT, AI in human interaction and AI in education.
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 limitations, risks and success metrics for a wide range of AI applications.
Match appropriate sensing imaging for different AI applications.
Select appropriate experiments to evaluate model robustness.
Apply different principles of experimental design.