AI Foundation ★ Foundation Level
Discover the fundamentals of AI including topics from opportunity identification and solution implementation, to algorithm development and AI engineering. This course is for anyone who wants to really understand why and how to apply AI in their work. No programming skills are required.
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Language
English
Duration
2 days
Time
9:00-17:00
Certification
Yes
Lunch
Included
Recommended Level
Foundation
Upcoming courses

AI Foundation

About the course

The AI Foundation is a 2-day course designed to provide you with a complete perspective of AI. You will be guided and challenged to identify and capture AI opportunities while managing risks like biased or polluted data, undesired differentiation, and corrupt algorithmic learning approaches. We apply AI cases with real data and machine learning algorithms so you can experience what developing and operating AI models entails. You will learn to better understand the process and what questions to ask to challenge the outcome and quality. The course is packed with industry insights and terminology, as well as practical challenges. It is developed by AI experts with extensive experience in teaching and implementing AI at the heart of many global top 500 companies, such as eBay and Heineken.  

Why this is for you

AI is taking off and becoming more important every year in key processes across all organizations. This means that no matter your background or role, the chances are high that your work will change in the near future due to new developments in the field of AI. The AI Foundation will guide you through this transitional phase and provide you with the tools and knowledge to grow responsibly with AI.
“I really feel like actually starting a use case. Let’s walk the talk!” Bob Dunselman, Senior Transfer Pricing Specialist at HEINEKEN

For whom

This course is for anyone who wants to really understand and benefit from AI. It is relevant for all layers within the organization, and requires no programming skills. And with more than a decade of experience in teaching AI, we know how to tailor the course to the unique needs of each group to derive the most value and impact for participants and organizations.  

What you’ll learn

The AI Foundation course is based on 4 major learning blocks:
  1. AI concepts and opportunities
Understand the significance of AI and how it compares to traditional statistical methods. Learn the universal terminology and language of AI. And translate these lessons into identifying opportunities in your own business, through leading case examples and exercises.
  1. AI algorithms demystified
Learn the fundamental steps to develop and manage AI algorithms and the key questions to pose at each stage of the AI development cycle. Above all, acquire the tools to establish and challenge the quality and fairness of models.
  1. The AI technology platform
Learn about the drivers of AI innovation in the technology landscape and how companies make AI operationalization possible. Become familiar with the AI engineering skills and technical developments, crucial for anyone working with AI.
  1. Creating impact and change with AI
Understand all the aspects required to lead and guide AI change in your business, including how to accelerate and scale AI solutions and build an organization with the right AI skills. Learning Goals
  • One language – Being able to have a conversation with anyone in the company about AI using the same terminology
  • Be inspired by AI – Walking away with at least one impactful idea on AI for your department/company
  • Experiencing the analytical process – Demystifying analytical models with hands-on experience and a toolbox for asking questions
  • Understanding the data technology – The steps in the technological process from data storage to the application layer
  • Creating impact – Understanding how to realize impact with AI: the change process and its implications for the organization and its way of working
  Theory and practical use All trainings in the GAIn portfolio combine high-quality standardized training material with theory sessions from experts and hands-on experience where you directly apply the material to real-life cases. Each training is developed by top of the field practitioners which means they are full of industry examples along with practical challenges and know-how, fueling the interactive discussions during training. We believe this multi-level approach creates the ideal learning environment for participants to thrive.

AI quick scan

Test how your company is performing on developing and implementing AI solutions and platform capabilities:

 

1.My organisation communicates a clear end-state vision on how AI can transform our business
2.AI initiatives are aligned with the strategic goals of my organisation
3.My organisation has a roadmap that focuses data analytics efforts on large scale business opportunities
4.My organisation succesfully builds high potential proof of concepts with AI
5.AI use cases are developed end-to-end from data to algorithm to an application, such that users can interact with the algorithm
6.AI use cases are automated and operationalized in a production environment
7.AI use cases are fully adopted by the business, without unintended retreat to own judgement
8.AI use cases change the business process fundamentally if adopted
9.AI use case MVP's are continuously being improved, leaving MVP state
10.My organisation succesfully scales impact of AI use cases
11.There is an infrastructure in place for users to approach (a selection of) data
12.There is a possibility to develop models, for example in sandbox like environments
13.Algorithms are operationalized and automatically predict on new data
14.Your organisation has deployed one ore more experiments with modern, for example cloud-based, infrastructure
15.AI platform standards are set and managed
16.Clarity exists on target infrastructure for new solutions
17.Existence of legacy is accepted as a given, but migrations and dependencies are managed with a impact driven mindset
18.AI solutions are in production on scalable modern infrastructure
19.There are standardized operational processes to develop, deploy and maintain models (model management) in modern infrastructure.
20.Users enjoy flexibility to access any data and use it to build solutions with diverse requirements, while still maintaining certain standards managed centrally