[Video introduction] Developing a machine learning strategy designed to maximize business value in the age of Deep Learning
Deep Learning is so dominant in some discussions of AI and machine learning that many organizations feel that they need to try to keep up with the latest trends. But does it offer the best path for your organization? What is this technology all about and why should both executives and practitioners understand its history?
All business leaders know that they have to embrace analytics or be left behind. However, technology changes so rapidly that it is difficult to know who to hire, which technologies to embrace, and how to proceed. The truth is that traditional machine learning techniques are a better fit for more organizations than chasing after the latest trends. The hyped techniques are popular for a reason so leaders with a responsibility for analytics need to have a high-level understanding of them.
Learning objectives
- Learn what makes Deep Learning so powerful and what are its limitations
- Understand why for many use cases traditional machine learning continues to be a much better option
- Learn the use cases in established industries where machine learning is driving measurable value
- Learn the industries and use cases where Deep Learning has made recent revolutionary progress and why
- Discuss the implications of these approaches for hiring and managing your analytics teams
- Learn how to maximize the value of your analytics portfolio by choosing the right projects and assigning the ideal resources.