Artificial Intelligence / Machine Learning Workshop in Omaha, NE

Certificate: Course Completion Certificate

Language: English

Duration: 3 Days

Credits: 24

Refreshments: Snacks, Beverages and Lunch included in a classroom session

Course Delivery: Classroom

Course Description:

This course provides a comprehensive introduction to the exciting fields of Artificial Intelligence (AI) and Machine Learning (ML). Designed for students with varying levels of expertise, from beginners to those with some background in programming and mathematics, the course covers fundamental concepts, techniques, and applications of AI and ML.

Attendees will begin by exploring the foundational principles of AI, including its history, goals, and various approaches. They will gain an understanding of intelligent agents, problem-solving techniques, and the importance of knowledge representation. Emphasis will be placed on the ethical considerations surrounding AI development and deployment.

The course will then delve into the core concepts of Machine Learning, where students will learn about different types of learning algorithms, such as supervised, unsupervised, and reinforcement learning. Through hands-on exercises and projects, students will develop a practical understanding of how to apply these algorithms to real-world problems, including classification, regression, clustering, and reinforcement learning tasks.

Learning Objectives:

After the training, you will be able to:

• Describe Artificial Intelligence workloads and considerations
• Describe fundamental principles of machine learning
• Describe features of computer vision workloads
• Describe features of Natural Language Processing (NLP) workloads
• Describe features of conversational AI workload

Prerequisites:

Prerequisite is not required for taking this course. Any one can with some basic awareness of computing and internet concepts, and an interest in using AI services.

Specifically:

• Experience using computers and the internet.
• Interest in use cases for AI applications and machine learning models.
• A willingness to learn through hands-on exploration.

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Training course contents:

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