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Derek Wales
This is a one-hour course for nontechnical audiences so they can understand how GenAI models are trained with the basics of the data science process through a simple interactive demo where they don't have to install or download any software.
Register for this course and see more details by visiting:
OpenCourser.com/course/bsans5/genai
What's inside
Syllabus
Project Overview
Here you will describe what the project is about...give an overview of what the learner will achieve by completing this project.
Good to know
Good to know
Know what's good ,
what to watch for , and
possible dealbreakers
This interactive demo requires installation or downloads
Well-suited for those in non-technical fields
Course is part of a larger series
Save this course
Save GenAI Basics - How LLMs Work to your list so you can find it easily later:
Save
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Activities
Be better prepared
before
your course. Deepen your understanding
during
and
after
it. Supplement your coursework and achieve mastery of the topics covered
in GenAI Basics - How LLMs Work with these
activities:
Complete the beginner's guide to python programming
Show steps
Lay the necessary foundation by completing a tutorial for beginner programmers in Python.
Browse courses on
Python
Show steps
-
Find a beginner's guide to python programming.
-
Read through the tutorial
-
Attempt all the exercises
-
Complete the tutorial
Review basic statistics and probability
Show steps
Strengthen your foundation in statistics and probability to enhance your comprehension of GenAI model training.
Browse courses on
Statistics
Show steps
-
Review your notes on statistics and probability.
-
Take practice quizzes on statistics and probability.
-
Complete practice problems on statistics and probability.
Practice writing python code to manipulate data
Show steps
Sharpen your skills writing python code to prepare for the assignments in the course.
Browse courses on
Python
Show steps
-
Set up a coding environment.
-
Load data into a python environment
-
Write code to manipulate the data
-
Write code to visualize the data
Five other activities
Expand to see all activities and additional details
Show all eight activities
Complete the TensorFlow tutorial
Show steps
Develop proficiency with TensorFlow, an open-source machine learning library.
Browse courses on
TensorFlow
Show steps
-
Find the TensorFlow tutorial
-
Read the tutorial
-
Attempt all the exercises
-
Complete the tutorial
Read Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow
Show steps
Gain a deeper understanding of supervised and unsupervised learning algorithms, and how to apply them in python.
View
Hands-On Machine Learning with Scikit-Learn,...
on Amazon
Show steps
-
Read the book
-
Complete the exercises
-
Build a machine learning project
Complete the machine learning coding practice exercises in Python
Show steps
Apply your new knowledge and solidify your understanding of machine learning with these practice drills.
Browse courses on
Machine Learning
Show steps
-
Find a set of machine learning coding practice exercises
-
Attempt to solve the exercises
-
Check your solutions
-
Review the exercises
Volunteer at a local AI or machine learning organization
Show steps
Support the community while deepening your understanding of AI or machine learning.
Browse courses on
AI
Show steps
-
Find a local AI or machine learning organization
-
Contact the organization
-
Volunteer your time
Write a blog post about your experience learning about GenAI models
Show steps
Solidify your understanding of GenAI models by writing about them in a blog post.
Browse courses on
GenAI
Show steps
-
Choose a topic
-
Write the blog post
-
Publish the blog post
Complete the beginner's guide to python programming
Show steps
Lay the necessary foundation by completing a tutorial for beginner programmers in Python.
Browse courses on
Python
Show steps
Show steps
Show steps
- Find a beginner's guide to python programming.
- Read through the tutorial
- Attempt all the exercises
- Complete the tutorial
Review basic statistics and probability
Show steps
Strengthen your foundation in statistics and probability to enhance your comprehension of GenAI model training.
Browse courses on
Statistics
Show steps
Show steps
Show steps
- Review your notes on statistics and probability.
- Take practice quizzes on statistics and probability.
- Complete practice problems on statistics and probability.
Practice writing python code to manipulate data
Show steps
Sharpen your skills writing python code to prepare for the assignments in the course.
Browse courses on
Python
Show steps
Show steps
Show steps
- Set up a coding environment.
- Load data into a python environment
- Write code to manipulate the data
- Write code to visualize the data
Five other activities
Expand to see all activities and additional details
Show all eight activities
Complete the TensorFlow tutorial
Show steps
Develop proficiency with TensorFlow, an open-source machine learning library.
Browse courses on
TensorFlow
Show steps
Show steps
Show steps
- Find the TensorFlow tutorial
- Read the tutorial
- Attempt all the exercises
- Complete the tutorial
Read Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow
Show steps
Gain a deeper understanding of supervised and unsupervised learning algorithms, and how to apply them in python.
View
Hands-On Machine Learning with Scikit-Learn,...
on Amazon
Show steps
Show steps
Show steps
- Read the book
- Complete the exercises
- Build a machine learning project
Complete the machine learning coding practice exercises in Python
Show steps
Apply your new knowledge and solidify your understanding of machine learning with these practice drills.
Browse courses on
Machine Learning
Show steps
Show steps
Show steps
- Find a set of machine learning coding practice exercises
- Attempt to solve the exercises
- Check your solutions
- Review the exercises
Volunteer at a local AI or machine learning organization
Show steps
Support the community while deepening your understanding of AI or machine learning.
Browse courses on
AI
Show steps
Show steps
Show steps
- Find a local AI or machine learning organization
- Contact the organization
- Volunteer your time
Write a blog post about your experience learning about GenAI models
Show steps
Solidify your understanding of GenAI models by writing about them in a blog post.
Browse courses on
GenAI
Show steps
Show steps
Show steps
- Choose a topic
- Write the blog post
- Publish the blog post
Career center
Learners who complete GenAI Basics - How LLMs Work will develop knowledge and skills
that may be useful to these careers:
Machine Learning Engineer
Machine Learning Engineer
Machine Learning Engineers build and maintain machine learning models. This course would help you understand the basics of how these models are trained and would be particularly relevant to your work training models.
Statistician
Statistician
Statisticians collect, analyze, and interpret data. This course would help you understand the basics of how data science models are trained, which builds a foundation for your work developing statistical models.
Data Architect
Data Architect
Data Architects design and build data systems. This course would help you understand the basics of how data science models are trained, which is particularly relevant for your work designing systems to store and process the data used to train these models.
Data Scientist
Data Scientist
Data Scientists build models that businesses use to make decisions. This course would help you understand the basics of how these models are trained, which may be useful for your work building these models or for communicating with the data scientists on your team.
Data Analyst
Data Analyst
Data Analysts analyze data to help businesses make decisions. This course would help you understand the basics of how data science models are trained, which may be useful for your work building these models or for communicating with the data scientists on your team.
Software Engineer
Software Engineer
Software Engineers design, develop, and maintain software systems. This course would help you understand the basics of how machine learning models are trained, which may be useful for your work building software that uses these models.
Technical Writer
Technical Writer
Technical Writers create documentation for technical products. This course would help you understand the basics of how data science models are trained, which may be useful for your work writing documentation for these models.
Financial Analyst
Financial Analyst
Financial Analysts analyze financial data to make investment recommendations. This course would help you understand the basics of how data science models are trained, which may be useful for your work building models to predict financial trends.
Marketing Analyst
Marketing Analyst
Marketing Analysts analyze marketing data to improve the effectiveness of marketing campaigns. This course would help you understand the basics of how data science models are trained, which may be useful for your work building models to predict customer behavior.
Risk Analyst
Risk Analyst
Risk Analysts analyze risks to businesses and develop strategies to mitigate those risks. This course would help you understand the basics of how data science models are trained, which may be useful for your work building models to predict risks.
Product Manager
Product Manager
Product Managers manage the development and launch of new products. This course would help you understand the basics of how machine learning models are trained, which may be useful for your work understanding the capabilities and limitations of these models.
Solutions Architect
Solutions Architect
Solutions Architects design and implement technology solutions for businesses. This course would help you understand the basics of how data science models are trained, which may be useful for your work designing solutions that use these models.
Quantitative Analyst
Quantitative Analyst
Quantitative Analysts use mathematical and statistical techniques to analyze financial data. This course would help you understand the basics of how data science models are trained, which may be useful for your work building models to predict financial trends.
Business Analyst
Business Analyst
Business Analysts analyze business processes and develop solutions to improve efficiency. This course would help you understand the basics of how data science models are trained, which may be useful for your work understanding the potential applications of these models.
Operations Research Analyst
Operations Research Analyst
Operations Research Analysts use mathematical and statistical techniques to solve business problems. This course would help you understand the basics of how data science models are trained, which may be useful for your work using these models to optimize business processes.
For more career information including salaries, visit:
OpenCourser.com/course/bsans5/genai
Reading list
We've selected ten books
that we think will supplement your
learning. Use these to
develop background knowledge, enrich your coursework, and gain a
deeper understanding of the topics covered in
GenAI Basics - How LLMs Work.
Offers a hands-on approach to learning machine learning algorithms and techniques using Python. It serves as a valuable reference for understanding the building blocks of AI.
This comprehensive guide provides a step-by-step approach to implementing machine learning models using popular libraries like Scikit-Learn, Keras, and TensorFlow.
Save
Written by leading experts in the field, this book covers the fundamental concepts of deep learning, making it an excellent choice for grasping the theoretical underpinnings of AI technology.
Focuses on natural language processing techniques and provides a solid foundation for understanding how AI models work with language data.
Offers a comprehensive introduction to statistical learning algorithms used in AI models, providing a strong foundation for understanding the mathematical aspects of AI technology.
This online course provides a practical approach to learning deep learning concepts and techniques. It's a valuable resource for those interested in gaining hands-on experience with AI models.
This introductory textbook provides a theoretical foundation for machine learning algorithms. It's a valuable resource for understanding the mathematical principles behind AI technology.
Provides a practical guide to using R for machine learning tasks. It's a valuable resource for those looking to leverage R's statistical capabilities in AI applications.
Save
Explores the principles and applications of GANs, which are a type of deep learning model used in generative tasks such as image and text generation.
Provides a comprehensive overview of deep reinforcement learning, which combines deep learning with reinforcement learning to create AI agents that can learn and optimize their behavior.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/bsans5/genai
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Effort
1
Level
Introductory
Via
Coursera
Institution
Duke University
Instructor
Derek Wales
Language
English
Good to know
This interactive demo requires installation or downloads
Well-suited for those in non-technical fields
Course is part of a larger series
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