Getting Started with Machine Learning

Unravel the complexities of machine learning and AI in an approachable way in this course designed for any role or background.

Upcoming Classes

Dates
Mode
Location
Price
Jun 02Jun 03, 2024
Las Vegas, NV
Las Vegas, NV at AI Con USA
$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.
Description

Unravel the complexities of machine learning in a way that's approachable for everyone, regardless of technical background. In Getting Started with Machine Learning, 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
Anyone currently using GitHub (Basic to Advanced) or looking to adopt GitHub in their software development lifecycle.

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
  • Exercise #1: AI Fact or Fiction?
  • Environment setup for hands-on exercise

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 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
  • 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
Jun 02Jun 03, 2024
Las Vegas, NV
Las Vegas, NV at AI Con USA
$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 Getting Started with Machine Learning, 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
Anyone currently using GitHub (Basic to Advanced) or looking to adopt GitHub in their software development lifecycle.

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
  • Exercise #1: AI Fact or Fiction?
  • Environment setup for hands-on exercise

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 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
  • 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

Don't see a date that fits your schedule? Contact us for scheduling options at 929.777.8102


Price: $1,495 USD
Course Duration: 3 Days
Description

Unravel the complexities of machine learning in a way that's approachable for everyone, regardless of technical background. In Getting Started with Machine Learning, 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
Anyone currently using GitHub (Basic to Advanced) or looking to adopt GitHub in their software development lifecycle.

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
  • Exercise #1: AI Fact or Fiction?
  • Environment setup for hands-on exercise

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 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
  • 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 Getting Started with Machine Learning, 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
Anyone currently using GitHub (Basic to Advanced) or looking to adopt GitHub in their software development lifecycle.

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
  • Exercise #1: AI Fact or Fiction?
  • Environment setup for hands-on exercise

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 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
  • 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?

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

Certification with a Conference

Maximize Your Learning Potential

STAR Conference logo

Agile + DevOps Conference logo