Introduction
In this course, you will explore real-world use cases in artificial intelligence (AI), machine learning (ML), and generative artificial intelligence (generative AI) across a range of industries. These areas include healthcare, finance, marketing, entertainment, and more. You will also learn about AI, ML, and generative AI capabilities and limitations, model selection techniques, and key business metrics.
Welcome video
Video transcript
Artificial Intelligence (AI). Let’s dive into real-world use cases and applications.
Many industries use AI such as manufacturing, healthcare, education, retail, life science, transportation, media and entertainment, and more. AI transformed these industries by boosting employee productivity, fostering creativity, improving business operations, and enhancing customer experiences. Text summarization, code generation, image, video, and text generation, music creation, content localization, chatbots, virtual assistance, anomaly detection, maintenance assistance, and contact center analytics are ways AI transforms how businesses operate and make decisions. This course will uncover AI’s endless possibilities and transformative power to shape the world.
Learning objectives
- Identify examples of real-world AI applications.
- Recognize use cases and solutions where AI can address business needs.
- Determine when AI and ML solutions are not appropriate.
- Identify use cases that use ML techniques such as supervised, unsupervised, and reinforcement learning.
- Identify capabilities of generative AI.
- Identify challenges of generative AI.
- Identify factors to consider when selecting generative AI models.
- Determine business metrics for generative AI applications.
In the next lesson, you will review the definitions of AI, ML, deep learning, and generative AI.