Fundamentals of AI
ICAgile Professional - Foundations of AI (ICP-FAI)
Description
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.
Course Duration and Schedule
Two-Day Format
8:30 AM - 4:30 PM each day with a 1-hour lunch break and morning and afternoon breaks.
Three-Day Format
11:30 AM - 5:00 PM each day with afternoon breaks.
Upcoming Training
✓ Guaranteed to Run
| Course | Certification | Date | Location | Price | Register | |
|---|---|---|---|---|---|---|
| Fundamentals of AI |
|
Jul 7 - Jul 9, 2026 | Virtual Classroom | $1,495 | Register | |
| Fundamentals of AI |
|
Sep 15 - Sep 17, 2026 | Virtual Classroom | $1,495 | Register | |
| Fundamentals of AI—ICAgile Certification (ICP-FAI) |
|
Sep 20 - Sep 21, 2026 | STARWEST 2026 - Anaheim, CA | $1,595 | Register | |
| Fundamentals of AI |
|
Nov 3 - Nov 5, 2026 | Virtual Classroom | $1,495 | Register |
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!
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