Prerequisite: None
Keith McCormick
Principal Data Scientist
Further
Keith McCormick is a highly accomplished professional consultant, mentor, and trainer, having served as keynote and moderator at international conferences focused on analytics practitioners and leadership alike.
Keith has built predictive analytics models since the 1990s utilizing popular industry advanced analytics solutions such as IBM SPSS Statistics, IBM SPSS Modeler, KNIME, and other popular open-source machine learning tools. He’s also authored seven books on the effective use of predictive analytics software and techniques.
He has guided organizations to establish highly effective analytical practices across industries, including the public sector, media, marketing, healthcare, retail, finance, manufacturing, and higher education.
Keith serves as data science principal on the Data Science and AI team at Further, a business services and consulting firm.
Nothing dominates the technology news cycle more than AI in its many forms, and for data professionals, the discussion often mentions deep learning. But what are the use cases for this technology? How do you know whether the challenge you are currently facing might need deep learning?
In this course, Keith McCormick will teach students what deep learning is and how it has become the tool of choice for certain applications. Then the instructor will present a series of examples. Each will set up a business challenge, and the instructor will discuss the most common tools used to address the challenge, the connection to deep learning, and some of the successful applications that can be found in industry.
Examples will be drawn from classic deep learning use cases, including computer vision, natural language processing, and large language models. Specific tools will be explicitly referenced, and software examples will be shown.
You Will Learn
- What is new and not so new in contemporary machine learning
- A brief history of deep learning and why it has become so important to the practical application of AI
- How to identify and describe classic deep learning use cases and how to recognize them
- Why deep learning is so closely related to contemporary computer vision and which applications are currently being successfully used in industry
- The connection between deep learning and natural language processing (NLP) and which use cases have been most successfully addressed
- Large language models (LLMs), their connection to deep learning, and some of the use cases associated with them
Geared To
- Analytics leaders who want to identify profitable opportunities for AI
- Data professionals who want to better understand deep learning and AI projects
- Data scientists
- Machine learning engineers
- Data engineers