Artificial Intelligence and Machine Learning – An Introduction

Discussions around the use of artificial intelligence (AI) have exploded in the last six months. If you are looking to follow the promise and the threat of AI, this is the course.

The application of AI, machine learning (ML) and deep learning to health care, finance, engineering and many others is accelerating expectations for success in business and education. When used in one of the many implementation methods, AI results in new ideas for product design, increased factory operation efficiency, improved health care diagnosis and, in general, more cost-effective business operations. In this course, the primary artificial intelligence methods, expert systems, genetic algorithms and neural networks are described along with the associated data which must be available for their respective implementations. In addition, the deep learning extension of neural networks is explored.

Some of the many approaches to software implementation are demonstrated particularly those using the Python software language. This is a hands-on class using Python to work with algorithms and data.

Instructor

Dennis Miller

Dennis Miller has a BS and MS in electrical engineering with additional graduate work in computer science. He worked for 35 years for Johnson Controls, Inc. as an engineer and manager in building controls research, product development and software testing. ... read more

Who Should Attend

Data analysts and engineers seeking to understand the uses and implementation of artificial intelligence and machine learning. Prior completion of the Data Analysis Certificate is not required. Participants may choose to take this course on its own or take all courses listed to earn the Data Analysis – Advanced Certificate.

Benefits and Learning Outcomes

• Gain insight into the primary approaches to AI and the types of business problems to which they can be potentially applied.
• Experience machine learning, deep learning and some of the primary software tools available for implementation.
• Explore the importance of quality data to any AI process and some approaches to data analysis and modification for AI use.

Course Outline/Topics

• Introduction, history and application methods of artificial intelligence (AI)
• Data requirements for AI methods and data preparation methods
• Expert systems description and exercises
• Genetic algorithm description and exercises
• Artificial neural network description and exercises
• Examples of applications

Prerequisites

Participants should have experience with basic data handling. Prior experience with Python is helpful, but not required.