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Generative AI Fluency

Course

This course provides a comprehensive high-level overview of Generative AI, beginning with foundational concepts and terminology and then delving into specific applications such as Large Language Models (LLMs) for text generation and diffusion-based models image creation. Key lessons include an in-depth look at LLMs, AI image generation methods, and hands-on experience with tools like DALL-E and Midjourney. The course concludes by addressing practical aspects of deploying Generative AI in production environments, focusing on data collection, prompt execution, maintenance, and orchestration strategies.

This course provides a comprehensive high-level overview of Generative AI, beginning with foundational concepts and terminology and then delving into specific applications such as Large Language Models (LLMs) for text generation and diffusion-based models image creation. Key lessons include an in-depth look at LLMs, AI image generation methods, and hands-on experience with tools like DALL-E and Midjourney. The course concludes by addressing practical aspects of deploying Generative AI in production environments, focusing on data collection, prompt execution, maintenance, and orchestration strategies.

Fluency

7 hours

Completion Certificate

Last Updated February 7, 2024

Skills you'll learn:
Generative AI Fluency
Prerequisites:

No experience required

Course Lessons

Lesson 1

Simple Introduction to Generative AI

In this lesson we will cover the key concepts, basic terminology, and typical applications of Generative AI.

Lesson 2

Large Language Models (LLMs) and Text Generation

In this lesson, you'll dive deeper into one of the most prominent applications of Generative AI: Large Language Models (LLMs).

Lesson 3

Introduction to AI Image Generation

In this lesson, you will learn what AI imaging generation is, how it works, and some AI image generation models and techniques.

Lesson 4

AI Image Generation Tools

In this lesson, you will learn how to generate images from text with some popular tools, like DALL-E and Midjourney.

Lesson 5

Generative AI in Production

In this lesson, you will learn about the practical considerations for using generative AI in production, including techniques for data collection, prompt execution, maintenance, and orchestration.

Taught By The Best

Photo of Uohna Thiessen

Uohna Thiessen

Data Scientist/AI Interaction Strategist

Dr. Thiessen is an experienced Data Scientist who has worked for various big tech companies, most recently at Meta. She holds a PhD in Epidemiology from Walden University and has taught data science subjects, including ML, NLP, DL & AI, at colleges and bootcamps across the nation.

Photo of Rohan Viswanathan

Rohan Viswanathan

ML Researcher

Rohan Viswanathan is a student at UC Berkeley studying Electrical Engineering and Computer Science. He is the VP of Education at Machine Learning @ Berkeley, and does research on autonomous navigation. He has previous experience doing software and machine learning development for companies like Tesla and Autodesk.

Photo of Giacomo Vianello

Giacomo Vianello

Principal Data Scientist

Giacomo Vianello is an end-to-end data scientist with a passion for state-of-the-art but practical technical solutions. He is Principal Data Scientist at Cape Analytics, where he develops AI systems to extract intelligence from geospatial imagery bringing, cutting-edge AI solutions to the insurance and real estate industries.

Photo of Raphael Mallari

Raphael Mallari

Video Producer at Udacity | Freelance Video Editor

Raphael is a video producer at Udacity. He has 10 years of video production experience, and specializes in video editing and post-production. As someone who works mainly with video software, he explores and experiments with new tools and methods of pushing creativity in videos. He has a BA and MFA in film from UCSC and SFSU, and is on his way on acquiring an MBA from SJSU.

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Ed Wiley, Ph.D.

AI Expert/Consultant at Ed Wiley Ventures, LLC

Dr. Ed Wiley has over 25 years of experience building, leading, and advising world-class machine learning, AI, and data science teams at companies at stages from startup to Fortune 50, holding titles such as CIO, CTO, and Chief Data Scientist. He currently serves as Chief Data and Analytics Officer for Opsis Health.

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Erick Galinkin

Principal AI Researcher

Erick Galinkin is a hacker and computer scientist, leading research at the intersection of security and artificial intelligence at Rapid7. He has spoken at numerous industry and academic conferences on topics ranging from malware development to game theory in security.

Photo of Chang She

Chang She

CEO and Co-founder of LanceDB

Chang has nearly two decades of experience building and teaching data / ML tooling. He was the second major contributor to pandas, an adjunct at Columbia for introduction to data science, and ran engineering at TubiTV focusing on recommender systems. Most recently, Chang co-founded LanceDB to build the next generation database for AI.

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Sergei Kozyrenko

Senior Staff Engineer

Sergei Kozyrenko is a technology leader with over 20 years of diverse industry experience - he’s built trading engines, banking software, learning management systems, co-founded an AI startup that accurately predicted street parking availability and even automated shooting of high-powered lasers at blocks of chocolate.

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Emily McMilin

Research Scientist

Emily McMilin is a Senior Research Scientist and Independent Researcher working at the intersection of NLP and Causal Inference. She obtained her Ph.D. in Electrical Engineering from Stanford University and prior to that an M.Sc. from University of Victoria, and a B.Sc. from Stanford in Symbolic Systems.

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