Artificial intelligence: business strategies and applications
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Course Details
- Introduction
- Objective
- Who should attend
- Course Location
Artificial Intelligence (AI) enables organisations to work faster, smarter; doing more with less. When aligned to real business opportunities and challenges, AI can be a transformative technology; creating new features or products, revolutionizing business processes and strategy, and creating new value for customers.
With artificial intelligence, we can build thousands of computers that could all work in unison to solve our most complex problems. It is also capable of seeing patterns in data that even trained professionals don’t always catch. Artificial intelligence and machine learning technologies can automate important, but manual and time-consuming tasks, allowing employees to focus on higher-value work. AI will be used to extract new insights, transform decision making, and drive improved business outcomes. Early adoption of artificial intelligence for specific, clearly defined applications enables forward-looking organizations to create significant business value and, ultimately, to set the stage for transforming business models and processes.
Artificial intelligence: business strategies and applications training course is designed to give managers an understanding of the growing deployment of AI in business, so they can appreciate what it can and cannot do for their organisation.
The programme also provides practical templates to guide how you work with data scientists and programmers in your organisation in making the most of these emerging technologies. Uniquely, it also features hands-on sessions where you will be shown how to commission analysis and analyse the results that data scientists produce.
Course Outline
Artificial intelligence ecosystem
- Explore the history and potential of AI within the context of the digital ecosystem.
AI and Machine learning
- Understanding the black box: Delve into the mechanics of the three main types of machine learning: supervised, reinforcement, and unsupervised learning.