The Best Online "Intro to AI" Course - Wharton School, U. Penn

You can search hundreds of free and paid options for starting your AI education and end up more confused than when you started. After reviewing dozens of the most popular sites and sources, the best course I can recommend for beginners is "AI Fundamentals for Non-Data Scientists" from the University of Pennsylvania ($79). An unlikely title for a "beginner" recommendation but hear me out.

My mother says there is a different shoe, a perfect shoe, for every occasion. Hiking or thrifting, take your pick. But one type of shoe is not good for both. The same is true with AI. Chat GPT and Gemini are not good for every occasion. But if we want to architect the best AI tool kit for communications teams it's essential to understand when, why, and how different AI apps work better in some situations than others. A non-technical understanding of how AI works behind the scenes will help you choose the right AI tool at the right time for the right occasion.

For example, we may never know exactly how a "random forest" algorithm is used to generate content, but knowing our chosen AI app creates the best possible content because it uses a random forest can be the difference between messaging that thrills and messaging that...doesn't. Thinking big picture, our AI strategy for comms will have to align with AI strategies (methods and tools) used in other parts of a startup or large enterprise. Explaining the "why" behind each AI choice will be essential during those discussions.

All that said, here is a summary of the "AI Fundamentals for Non-Data Scientists" course offered by the Wharton School at the University of Pennsylvania. The topics may seem heavily technical based on the descriptions below provided by U. Penn, but I promise anyone can understand the content.

Course Description: AI Fundamentals for Non-Data Scientists

Go in-depth to discover how Machine Learning is used to handle and interpret Big Data. You will get a detailed look at the various ways and methods to create algorithms to incorporate into your business with such tools as Teachable Machine and TensorFlow; learn different ML methods, Deep Learning, as well as the limitations but also how to drive accuracy and use the best training data for your algorithms; explore GANs and VAEs, using your newfound knowledge to engage with AutoML to help you start building algorithms that work to suit your needs; see exclusive interviews with industry leaders, who manage Big Data for companies such as McDonald's and Visa. You will have learned different ways to code, including how to use no-code tools, understand Deep Learning, how to measure and review errors in your algorithms, and how to use Big Data to not only maintain customer privacy but also how to use this data to develop different strategies that will drive your business.

Course Structure and Content

Module 1: Introduction to AI and Big Data

• This module covers the basics of AI and Big Data, explaining how these technologies are transforming various business sectors. It introduces machine learning (ML) and how data is analyzed and extracted using digital technologies.

Module 2: Machine Learning Algorithms

• Learners delve into different ML methods, including logistic regression and neural networks. The module also covers Deep Learning, its applications, and limitations. Students learn about optimizing algorithms, evaluating performance, and understanding loss functions.

Module 3: Emerging Methods in ML

• This section focuses on advanced topics like natural language processing (NLP) and generative modeling (GANs and VAEs). It also introduces AutoML and no-code tools like Teachable Machine to simplify the creation and deployment of ML models.

Module 4: Industry Applications and Insights

• Featuring interviews with industry leaders, such as the VP of Global Menu Strategy & Global Marketing at McDonald's, this module provides practical insights into how AI and Big Data are used in real-world business scenarios. Learners explore strategies for managing data privacy and utilizing Big Data to drive business decisions.

Learning Outcomes

By the end of the course, participants will:

• Understand the basic principles of AI and ML.

• Be able to apply ML methods and algorithms to business problems.

• Gain proficiency in using tools like TensorFlow and Teachable Machine.

• Learn from industry experts about the practical applications of AI in various sectors.

Benefits and Feedback

The course is praised for its clarity, practical examples, and the expertise of its instructors. It is particularly beneficial for those looking to integrate AI into their business strategies without delving into the technical complexities of data science.

Additional recommended courses from Wharton include (all are $79):

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