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 - Fundamentals 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!

✓ = Guaranteed to Run

Dates

Mode

Location

Price

Jun 02—Jun 03, 2024 ✓

Public Classroom - AI Con USA

Las Vegas, NV

Las Vegas, NV at AI Con USA

$1,545

ATTEND

Sep 22—Sep 23, 2024

Public Classroom - STARWEST

Anaheim, CA

Anaheim, CA at STARWEST

$1,545

ATTEND

Oct 13—Oct 14, 2024

Public Classroom - Agile + DevOps East

Orlando, FL

Orlando, FL at Agile + DevOps East

$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 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

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

Dalton Forbes

✓ = Guaranteed to Run

Dates

Mode

Location

Price

Jul 16—Jul 17, 2024

Live Virtual

Virtual Classroom

$1,495

Attend

Oct 08—Oct 09, 2024

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 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.

✓ = Guaranteed to Run

Dates

Mode

Location

Price

Anytime

Your Location

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 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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Jeffery Payne

Dalton Forbes

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Fundamentals of AI—ICAgile Certification (ICP-FAI)

Upcoming Classes

Jeffery Payne

Dalton Forbes

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

Questions?

On-Site/Private Training

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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