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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.

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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

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

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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
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
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
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
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Show all eight activities
Complete the TensorFlow tutorial
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
Gain a deeper understanding of supervised and unsupervised learning algorithms, and how to apply them in python.
Show steps
  • Read the book
  • Complete the exercises
  • Build a machine learning project
Complete the machine learning coding practice exercises in Python
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
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
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

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 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
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 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 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 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 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 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 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 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 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 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 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 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 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 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.

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.
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.
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.

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