AI Fundamentals for Non-Data Scientists

In this module, you will get an in-depth look at contrasting Machine Learning methods, including logistic regression and neural nets. You will also learn about Deep Learning and its relationship to neural networks and how to best optimize Machine Learning algorithms. Lastly, you will be introduced to loss functions and how to best measure and review errors to maintain the integrity of your algorithms. By the end of this module, you will have learned about Machine Learning methods, the limitations and value of Deep Learning, how best to drive precision and accuracy in algorithms, and how to get the best training data for those algorithms.

In this module, you will take a look at Machine Learning within natural language processing and using generative modeling to create new data. You will also focus on AutoML and how to best utilize automated processes to make your algorithms more efficient. You will also review the no-code Machine Learning tool Teachable Machine, which serves to make Deep and Machine Learning more accessible. By the end of this module, you will be able to use AutoML in your algorithms and be able to navigate and use Teachable Machine in practice for no-code solutions to building an algorithm.

In this module, you will hear from an industry leader and gain valuable insight into data sampling and building realistic usable models. Ed Lee, VP of Global Menu Strategy & Global Marketing at McDonald's, will allow you to review real-world solutions and how they handle data issues as one of the most successful global brands. By the end of this module, you will have heard from a top industry expert in their field and gained firsthand knowledge and understanding of how Big Data plays into maintaining privacy in data and also utilizing that data to enhance your marketing, content, and refine your algorithms.

In this module, you will explore multiple aspects of generative AI. Not only will you gain an understanding of how it makes predictions and generates content, but you will also gain an understanding of how large language models work. Diving deeper, you will explore the generative AI stack as well as foundation models and their versatility in performing a broad range of tasks. Reflecting on research studies, you will examine the implications of generative AI on work and productivity, including the potential for both human displacement and enhancement. You will gain insights for crafting instructions to improve the quality of output from large language modules and explore how a company building an application on top of foundation models may gain a competitive advantage.