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Fundamentals of AI—ICAgile Certification (ICP-FAI)

Unravel the complexities of machine learning in a way that's approachable for everyone, regardless of technical background. In Fundamentals of AI, participants embark on a journey through the world of machine learning, understanding not only the what but the why and how behind this transformative technology.

Rather than diving deep into complex algorithms, this course takes a practical approach to machine learning. Participants engage in hands-on activities and discussions that showcase the real-world applications of machine learning in various industries including healthcare, finance, recommendation systems, and more.

Key takeaways from this class include:

  • Breaking down the notion of machine learning into simple, relatable terms, offering a glimpse into how machines learn from data.
  • Understanding the types of machine learning and their impact on everyday life.
  • Demystifying machine learning algorithms by explaining their functions using familiar examples.
  • Understanding critical ethical considerations like biases, fairness, and the responsible use of AI.

By the end of this course, participants emerge equipped with a clear understanding of the fundamental principles of machine learning, enabling them to engage in informed discussions and make sense of the pervasive role of this technology in our rapidly evolving world.

Who Should Attend
This class is for anyone, technical or non-technical, who wants to understand the applicability of machine learning to everyday life. It is also for anyone who wants to learn the basics of machine learning and how to interact with Generative AI tools such as ChatGPT or Windows Copilot.

Course Completion and Certification
Upon completion of this course the attendee will be certified by the International Consortium for Agile (ICAgile) and awarded the ICAgile Professional - Foundations of AI (ICP-FAI) designation. The ICAgile certification fee is included with your registration for your convenience.

About the ICAgile
The International Consortium for Agile’s goal is to foster thinking and learning around agile methods, skills, and tools. The ICAgile, working with experts and organizations across agile development specialties, has captured specific learning objectives for the different agile development paths and put them on the learning roadmap. For more information visit www.icagile.com.

Course Outline

Session 1: Introduction to AI and Machine Learning

  • History and types of AI
  • Definitions of AI, ML, LLM, and deep learning
  • Importance and applications of machine learning
  • Challenges in AI and ML
    • Accuracy of results
    • Ethics and bias in machine learning
    • Safety and security
    • Intellectual property
    • Governance
  • Exercise #1: See How Far We've Come

Session 2: Types of Learning

  • How learning happens in ML
  • What is supervised learning?
  • Classification vs. Regression
  • Object detection
  • Model evaluation and metrics for classification and regression
  • Exercise #2: Model output evaluation
  • What is unsupervised learning?
  • Clustering algorithms (K-Means, Hierarchical, DBSCAN) examples
  • Model evaluation and metrics for clustering
  • Dimensionality reduction (PCA, t-SNE)
  • What is reinforcement learning?
  • Reinforcement learning approaches
  • Transfer learning
  • Exercise #3: Choosing the most appropriate type of learning

Session 3: Understanding the Machine Learning Process

  • Overall development process
  • AI and Agile
  • AI cross-functional teams
  • Data and model management
  • Model engineering
  • Model testing and deployment
  • Model serving and monitoring
  • Exercise #4: End-to-end ML process
  • Machine learning business challenges
  • MLOps: DevOps for ML
  • Tools support MLOps
  • Exercise #5: Running MLOps tools

Session 4: Neural Networks and Deep Learning

  • Introduction to artificial neural networks
  • How these networks work
  • Deep learning and large language models (LLM)
  • Key deep learning architectures (FNNs, CNNs, RNNs, GANs)
  • Transformer architectures
  • Fine-tuning existing models
  • Exercise #6: Using LLMs for classification

Session 5: Generative AI and Prompt Engineering

  • What is Generative AI?
  • Application of Generative AI
  • Understanding prompt engineering
  • Using prompt engineering techniques
  • Prompt engineering best practices
  • Using COSTAR for better prompting
  • Generative AI challenges
  • Exercise #7: Use prompt engineering

Session 6: Using AI as a Competitive Edge

  • Competitive advantages of AI
  • Using AI to optimize operations
  • AI to enhance customer engagement
  • Building an AI strategy
  • AI investments
  • AI governance
  • AI maturity model
  • Challenges and smoothing transition

Wrap up & Next steps

  • Exercise #8: Evaluate Your AI Maturity
  • Resources for further learning (books, online courses, communities)Recap of key concepts and skills learned
  • Course discussion and ‘AHA Moments’
  • Course evaluation
  • Next steps
  • Thank you!