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.

Questions? 929.777.8102 [email protected]

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!

✓ = Guaranteed to Run

Dates

Mode

Location

Price

Mar 24—Mar 25, 2025

Public Classroom - AI Training Week DC

Microsoft Innovation Hub, Arlington, VA

Microsoft Innovation Hub, Arlington, VA at AI Training Week DC

$1,545

ATTEND

Apr 27—Apr 28, 2025

Public Classroom - STAREAST

Orlando, FL

Orlando, FL at STAREAST

$1,545

ATTEND

Download Fact Sheet

Price: $1,545 USD

Course Duration: 2 Days

Description

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.

Questions? 929.777.8102 [email protected]

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!

Class Schedule

Sign-In/Registration 7:30 - 8:30 a.m.
Morning Session 8:30 a.m. - 12:00 p.m.
Lunch 12:00 - 1:00 p.m.
Afternoon Session 1:00 - 5:00 p.m.
Times represent the typical daily schedule. Please confirm your schedule at registration.

Class Fee Includes

• Digital course materials
• Continental breakfasts and refreshment breaks
• Lunches

Instructors

Jeffery Payne

Spotlight

Jeffery Payne has led Coveros since its inception in 2008. Under his guidance, the company has become a recognized market leader in secure agile software development. He is a popular keynote and featured speaker at technology conferences and has testified before Congress on technology issues such as intellectual property rights...Learn More

Jeff Pierce

Jeff Pierce has more than 20 years of experience in software engineering management, specializing in program, project, and software quality assurance management. Jeff is also experienced in building technical teams that are rapidly able to implement solutions and deliver business value. More recently, Jeff has focused on Agile project management...Learn More

✓ = Guaranteed to Run

Dates

Mode

Location

Price

Jan 14—Jan 16, 2025

Live Virtual

Virtual Classroom

$1,495

Attend

Feb 04—Feb 06, 2025

Live Virtual

Virtual Classroom

$1,495

Attend

Mar 04—Mar 06, 2025

Live Virtual

Virtual Classroom

$1,495

Attend

Apr 08—Apr 10, 2025

Live Virtual

Virtual Classroom

$1,495

Attend

May 06—May 08, 2025

Live Virtual

Virtual Classroom

$1,495

Attend

Jun 03—Jun 05, 2025

Live Virtual

Virtual Classroom

$1,495

Attend

Download Fact Sheet

Price: $1,495 USD

Course Duration: 3 Days

Description

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.

Questions? 929.777.8102 [email protected]

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!

Class Fee Includes

Easy course access: Attend training right from your computer and easily connect your audio via computer or phone. Easy and quick access fits today’s working style and eliminates expensive travel and long days in the classroom.
Live, expert instruction: Instructors are sought-after practitioners, highly-experienced in the industry who deliver a professional learning experience in real-time.
Valuable course materials: Courses cover the same professional content as our classroom training, and students have direct access to valuable materials.
Rich virtual learning environment: A variety of tools are built in to the learning platform to engage learners through dynamic delivery and to facilitate a multi-directional flow of information.
Hands-on exercises: An essential component to any learning experience is applying what you have learned. Using the latest technology, your instructor can provide hands-on exercises, group activities, and breakout sessions.
Real-time communication: Communicate real-time directly with the instructor. Ask questions, provide comments, and participate in the class discussions.
Peer interaction: Networking with peers has always been a valuable part of any classroom training. Live Virtual training gives you the opportunity to interact with and learn from the other attendees during breakout sessions, course lecture, and Q&A.
Small class size: Live Virtual courses are limited in small class size to ensure an opportunity for personal interaction.

Bring this course to your team at your site. Contact us to learn more at 929.777.8102.

✓ = Guaranteed to Run

Dates

Mode

Location

Price

Call to Schedule

Anytime

On-Site / Private

Your Location

Request a Quote

Download Fact Sheet

Course Duration: 3 Days

Description

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.

Questions? 929.777.8102 [email protected]

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!

Questions?

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Bring this course to your team at your site. Contact us to learn more at 929.777.8102.

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Fundamentals of AI—ICAgile Certification (ICP-FAI)

Upcoming Classes

Jeffery Payne

Jeff Pierce

Bring this course to your team at your site. Contact us to learn more at 929.777.8102.

Questions?

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Let us bring the learning to your team at your location or in an interactive virtual classroom!Choose from more than 50 courses.

Combine World-Class Training and

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