Fundamentals of AI—ICAgile Certification (ICP-FAI)

Unravel the complexities of machine learning and AI. Attendees will be awarded the ICAgile Professional- Foundations of AI (ICP-FAI) designation. 

Upcoming Classes

Dates
Mode
Location
Price
Jul 17Jul 18, 2024
Virtual Classroom
Virtual Classroom
$1,495
Oct 08Oct 09, 2024
Virtual Classroom
Virtual Classroom
$1,495
Sep 22Sep 23, 2024
Anaheim, CA
Anaheim, CA at STARWEST
$1,545
Oct 13Oct 14, 2024
Orlando, FL
Orlando, FL at Agile + DevOps East
$1,545
Call to Schedule
Anytime
Your Location
Your Location
Select a learning mode button (Public, Live Virtual, etc.) for pricing, details, and a downloadable fact sheet.
Ways to Save

Software Summer School - BOGO - JUNE, JULY, & AUGUST CLASSES

Our Software Summer School is back in session! Buy One, Get One Free on our most popular live virtual training classes offered between June 1 and August 31, 2024. Take both classes yourself or split them with a colleague.

Our practical, hands-on courses can help you and your team in areas like AI, GitHub, agile, leadership, DevOps, software testing, and many more. Live virtual training allows you to dive deep into these topics with instructor-led classes from the comfort of your home or office.

Ready to join? Click the Register button below to register via email.

This is a limited time offer, and certain restrictions do apply. See details below.

OFFER DETAILS: Register for any participating live virtual class being offered in June, July, or August 2024 and get a second class of equal or lesser value free. Classes must take place between June 1 and August 31. Not all classes are valid. Contact Client Support for specifics on which classes are included. Register directly with our Client Support team at 929.777.8102 or through the email above. This offer is only valid for new registrations and cannot be combined with other promotions. Optional certification test vouchers must be purchased one per person who wishes to add them on and are NOT included in the Buy 1 Get 1 Free deal that is limited to just the class.
 
PREQUISITES: Many of our classes require previous skills, experience, or certifications to ensure understanding. Please be sure to check prerequisites for each class to ensure you have the experience necessary to actively participate. Questions? Call us at 929.777.8102.
 
Choose from the following classes.  ( = Guaranteed to run dates):
 
June Classes

July Classes

August Classes

 
  

 

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 AI & 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.

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.

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.

Questions? 929.777.8102 [email protected]
Course Outline

Session 1: Introduction to Machine Learning

  • Definitions of AI, ML, LLM, and deep learning
  • Importance and applications of machine learning
  • Overview of the machine learning workflow
  • Model interpretation and explainability
  • Ethics and bias in machine learning
  • Environment setup for hands-on exercise
  • Exercise #1: AI Fact or Fiction?

Session 2: Understanding the Machine Learning Process

  • Overall development process
  • Data engineering
  • Model selection, training, and validation
  • Testing and deployment
  • Operation, monitoring, retraining
  • MLOps
  • Exercise #2: Quiz on ML process

Session 3: Types of Learning

  • Types of machine learning (supervised, unsupervised, reinforcement)
  • What is supervised learning?
  • Classification vs. Regression
  • Decision trees and random forest examples
  • Model evaluation and metrics (accuracy, precision, recall, F1-score)
  • Exercise #3: Real-world example - classifying images
  • What is unsupervised learning?
  • Clustering algorithms (K-Means, Hierarchical, DBSCAN) examples
  • Dimensionality reduction (PCA, t-SNE)
  • Exercise #4: Real-world example - using clustering
  • What is reinforcement learning?
  • Reinforcement learning approaches
  • Exercise #5: Real-world example - Using reinforcement learning

Session 4: Neural Networks and Deep Learning

  • Introduction to artificial neural networks
  • How these networks work
  • Feedforward and back propagation
  • Deep learning and large language models (LLM)
  • Key deep learning architectures (FNNs, CNNs, RNNs, GANs)
  • Transformer architectures
  • Fine-tuning existing models
  • Exercise #6: Leveraging pre-existing models

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
  • Demonstration of Google Gemini
  • Demonstration of GitHub Copilot
  • Exercise #7: Creating good prompts with ChatGPT

Exercise #8: Putting it all together
 
Wrap up & Next steps

  • Recap of key concepts and skills learned
  • Course evaluation
  • Resources for further learning (books, online courses, communities)
  • Thank you!
Dates
Mode
Location
Price
Sep 22Sep 23, 2024
Anaheim, CA
Anaheim, CA at STARWEST
$1,545
Oct 13Oct 14, 2024
Orlando, FL
Orlando, FL at Agile + DevOps East
$1,545
Price: $1,545 USD
Course Duration: 2 Days
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 AI & 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.

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.

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.

 

Questions? 929.777.8102 [email protected]
Course Outline

Session 1: Introduction to Machine Learning

  • Definitions of AI, ML, LLM, and deep learning
  • Importance and applications of machine learning
  • Overview of the machine learning workflow
  • Model interpretation and explainability
  • Ethics and bias in machine learning
  • Environment setup for hands-on exercise
  • Exercise #1: AI Fact or Fiction?

Session 2: Understanding the Machine Learning Process

  • Overall development process
  • Data engineering
  • Model selection, training, and validation
  • Testing and deployment
  • Operation, monitoring, retraining
  • MLOps
  • Exercise #2: Quiz on ML process

Session 3: Types of Learning

  • Types of machine learning (supervised, unsupervised, reinforcement)
  • What is supervised learning?
  • Classification vs. Regression
  • Decision trees and random forest examples
  • Model evaluation and metrics (accuracy, precision, recall, F1-score)
  • Exercise #3: Real-world example - classifying images
  • What is unsupervised learning?
  • Clustering algorithms (K-Means, Hierarchical, DBSCAN) examples
  • Dimensionality reduction (PCA, t-SNE)
  • Exercise #4: Real-world example - using clustering
  • What is reinforcement learning?
  • Reinforcement learning approaches
  • Exercise #5: Real-world example - Using reinforcement learning

Session 4: Neural Networks and Deep Learning

  • Introduction to artificial neural networks
  • How these networks work
  • Feedforward and back propagation
  • Deep learning and large language models (LLM)
  • Key deep learning architectures (FNNs, CNNs, RNNs, GANs)
  • Transformer architectures
  • Fine-tuning existing models
  • Exercise #6: Leveraging pre-existing models

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
  • Demonstration of Google Gemini
  • Demonstration of GitHub Copilot
  • Exercise #7: Creating good prompts with ChatGPT

Exercise #8: Putting it all together

 

Wrap up & Next steps

  • Recap of key concepts and skills learned
  • Course evaluation
  • Resources for further learning (books, online courses, communities)
  • 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
Dates
Mode
Location
Price
Jul 17Jul 18, 2024
Virtual Classroom
Virtual Classroom
$1,495
Oct 08Oct 09, 2024
Virtual Classroom
Virtual Classroom
$1,495
Price: $1,495 USD
Course Duration: 3 Days
Ways to Save

Software Summer School - BOGO - JUNE, JULY, & AUGUST CLASSES

Our Software Summer School is back in session! Buy One, Get One Free on our most popular live virtual training classes offered between June 1 and August 31, 2024. Take both classes yourself or split them with a colleague.

Our practical, hands-on courses can help you and your team in areas like AI, GitHub, agile, leadership, DevOps, software testing, and many more. Live virtual training allows you to dive deep into these topics with instructor-led classes from the comfort of your home or office.

Ready to join? Click the Register button below to register via email.

This is a limited time offer, and certain restrictions do apply. See details below.

OFFER DETAILS: Register for any participating live virtual class being offered in June, July, or August 2024 and get a second class of equal or lesser value free. Classes must take place between June 1 and August 31. Not all classes are valid. Contact Client Support for specifics on which classes are included. Register directly with our Client Support team at 929.777.8102 or through the email above. This offer is only valid for new registrations and cannot be combined with other promotions. Optional certification test vouchers must be purchased one per person who wishes to add them on and are NOT included in the Buy 1 Get 1 Free deal that is limited to just the class.
 
PREQUISITES: Many of our classes require previous skills, experience, or certifications to ensure understanding. Please be sure to check prerequisites for each class to ensure you have the experience necessary to actively participate. Questions? Call us at 929.777.8102.
 
Choose from the following classes.  ( = Guaranteed to run dates):
 
June Classes

July Classes

August Classes

 
  

 

Ways to Save

Software Summer School - BOGO - JUNE, JULY, & AUGUST CLASSES

Our Software Summer School is back in session! Buy One, Get One Free on our most popular live virtual training classes offered between June 1 and August 31, 2024. Take both classes yourself or split them with a colleague.

Our practical, hands-on courses can help you and your team in areas like AI, GitHub, agile, leadership, DevOps, software testing, and many more. Live virtual training allows you to dive deep into these topics with instructor-led classes from the comfort of your home or office.

Ready to join? Click the Register button below to register via email.

This is a limited time offer, and certain restrictions do apply. See details below.

OFFER DETAILS: Register for any participating live virtual class being offered in June, July, or August 2024 and get a second class of equal or lesser value free. Classes must take place between June 1 and August 31. Not all classes are valid. Contact Client Support for specifics on which classes are included. Register directly with our Client Support team at 929.777.8102 or through the email above. This offer is only valid for new registrations and cannot be combined with other promotions. Optional certification test vouchers must be purchased one per person who wishes to add them on and are NOT included in the Buy 1 Get 1 Free deal that is limited to just the class.
 
PREQUISITES: Many of our classes require previous skills, experience, or certifications to ensure understanding. Please be sure to check prerequisites for each class to ensure you have the experience necessary to actively participate. Questions? Call us at 929.777.8102.
 
Choose from the following classes.  ( = Guaranteed to run dates):
 
June Classes

July Classes

August Classes

 
  

 

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 AI & 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.

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.

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.

Questions? 929.777.8102 [email protected]
Course Outline

Session 1: Introduction to Machine Learning

  • Definitions of AI, ML, LLM, and deep learning
  • Importance and applications of machine learning
  • Overview of the machine learning workflow
  • Model interpretation and explainability
  • Ethics and bias in machine learning
  • Environment setup for hands-on exercise
  • Exercise #1: AI Fact or Fiction?

Session 2: Understanding the Machine Learning Process

  • Overall development process
  • Data engineering
  • Model selection, training, and validation
  • Testing and deployment
  • Operation, monitoring, retraining
  • MLOps
  • Exercise #2: Quiz on ML process

Session 3: Types of Learning

  • Types of machine learning (supervised, unsupervised, reinforcement)
  • What is supervised learning?
  • Classification vs. Regression
  • Decision trees and random forest examples
  • Model evaluation and metrics (accuracy, precision, recall, F1-score)
  • Exercise #3: Real-world example - classifying images
  • What is unsupervised learning?
  • Clustering algorithms (K-Means, Hierarchical, DBSCAN) examples
  • Dimensionality reduction (PCA, t-SNE)
  • Exercise #4: Real-world example - using clustering
  • What is reinforcement learning?
  • Reinforcement learning approaches
  • Exercise #5: Real-world example - Using reinforcement learning

Session 4: Neural Networks and Deep Learning

  • Introduction to artificial neural networks
  • How these networks work
  • Feedforward and back propagation
  • Deep learning and large language models (LLM)
  • Key deep learning architectures (FNNs, CNNs, RNNs, GANs)
  • Transformer architectures
  • Fine-tuning existing models
  • Exercise #6: Leveraging pre-existing models

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
  • Demonstration of Google Gemini
  • Demonstration of GitHub Copilot
  • Exercise #7: Creating good prompts with ChatGPT

Exercise #8: Putting it all together

 

Wrap up & Next steps

  • Recap of key concepts and skills learned
  • Course evaluation
  • Resources for further learning (books, online courses, communities)
  • 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.

Dates
Mode
Location
Price
Call to Schedule
Anytime
Your Location
Your Location
Course Duration: 3 Days
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 AI & 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.

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.

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.

Questions? 929.777.8102 [email protected]
Course Outline

Session 1: Introduction to Machine Learning

  • Definitions of AI, ML, LLM, and deep learning
  • Importance and applications of machine learning
  • Overview of the machine learning workflow
  • Model interpretation and explainability
  • Ethics and bias in machine learning
  • Environment setup for hands-on exercise
  • Exercise #1: AI Fact or Fiction?

Session 2: Understanding the Machine Learning Process

  • Overall development process
  • Data engineering
  • Model selection, training, and validation
  • Testing and deployment
  • Operation, monitoring, retraining
  • MLOps
  • Exercise #2: Quiz on ML process

Session 3: Types of Learning

  • Types of machine learning (supervised, unsupervised, reinforcement)
  • What is supervised learning?
  • Classification vs. Regression
  • Decision trees and random forest examples
  • Model evaluation and metrics (accuracy, precision, recall, F1-score)
  • Exercise #3: Real-world example - classifying images
  • What is unsupervised learning?
  • Clustering algorithms (K-Means, Hierarchical, DBSCAN) examples
  • Dimensionality reduction (PCA, t-SNE)
  • Exercise #4: Real-world example - using clustering
  • What is reinforcement learning?
  • Reinforcement learning approaches
  • Exercise #5: Real-world example - Using reinforcement learning

Session 4: Neural Networks and Deep Learning

  • Introduction to artificial neural networks
  • How these networks work
  • Feedforward and back propagation
  • Deep learning and large language models (LLM)
  • Key deep learning architectures (FNNs, CNNs, RNNs, GANs)
  • Transformer architectures
  • Fine-tuning existing models
  • Exercise #6: Leveraging pre-existing models

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
  • Demonstration of Google Gemini
  • Demonstration of GitHub Copilot
  • Exercise #7: Creating good prompts with ChatGPT

Exercise #8: Putting it all together

 

Wrap up & Next steps

  • Recap of key concepts and skills learned
  • Course evaluation
  • Resources for further learning (books, online courses, communities)
  • Thank you!

Questions?

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