Artificial Intelligence

Back To AI Course List

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 1hr, 4hr or 15hr course.

If you’re interested in learning more about how to receive a micro-credential, click here.

Register Now

Card image cap
Focus Area

Engineering

There Are 3 Courses Available in This Package

1hr
Course

Intro to AI Applications

Register Now


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.

4hr
Course

Fundamentals of AI Applications

Register Now


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.

15hr
Course

AI Applications

Register Now


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.

Meet The Instructor

Instructor Headshot

Diego Alvarado

1hr, 4hr and 15hr Instructor
Instructor Headshot

Catia Silva, Ph.D.

1hr, 4hr and 15hr Instructor
Instructor Headshot

Paul Gader, Ph.D.

15hr Instructor

SHOWCASE MASTERY WITH A MICRO-CREDENTIAL.