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Laurence Moroney

AI pair programming is being rapidly adopted by developers to help with tasks across the tech stack, from catching bugs to quickly inserting entire code snippets.

Learn how LLMs can enhance, debug, and document your code in this new course built in collaboration with Google.

Get free access to Google’s PaLM API and get hands-on experience that you can apply to your own projects. You’ll learn how to use an LLM in pair programming to:

1. Simplify and improve your code

2. Write test cases

3. Debug and refactor your code

Read more

AI pair programming is being rapidly adopted by developers to help with tasks across the tech stack, from catching bugs to quickly inserting entire code snippets.

Learn how LLMs can enhance, debug, and document your code in this new course built in collaboration with Google.

Get free access to Google’s PaLM API and get hands-on experience that you can apply to your own projects. You’ll learn how to use an LLM in pair programming to:

1. Simplify and improve your code

2. Write test cases

3. Debug and refactor your code

4. Explain and document any complex code written in any coding language

Enroll now

What's inside

Syllabus

Pair Programming with a Large Language Model
AI pair programming is being rapidly adopted by developers to help with tasks across the tech stack, from catching bugs to quickly inserting entire code snippets. Learn how LLMs can enhance, debug, and document your code in this new course built in collaboration with Google.Get free access to Google’s PaLM API and get hands-on experience that you can apply to your own projects. You’ll learn how to use an LLM in pair programming to: (1) Simplify and improve your code. (2) Write test cases. (3) Debug and refactor your code. (4) Explain and document any complex code written in any coding language.

Good to know

Know what's good
, what to watch for
, and possible dealbreakers
Geared towards developers seeking to enhance their programming skills with AI integration
Taught by Laurence Moroney, a noted expert in the field of AI pair programming
Powered by Google's PaLM API, providing students with access to cutting-edge technology
Emphasizes hands-on experience, allowing students to apply their learning immediately
Covers a wide range of topics, including code simplification, test case writing, and debugging techniques

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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 Pair Programming with a Large Language Model with these activities:
Review Python programming fundamentals
Ensure a solid foundation in Python programming, which is essential for implementing AI pair programming techniques.
Browse courses on Python
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  • Revisit basic Python concepts such as data types, variables, and control flow.
  • Practice writing simple Python programs.
  • Review Python libraries commonly used in AI development.
Review 'Deep Learning for Coders with Fastai and PyTorch'
Gain a deeper understanding of the underlying principles of deep learning, which is a fundamental aspect of AI pair programming.
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  • Read the book and take notes on key concepts.
  • Implement the code examples provided in the book to practice your skills.
  • Apply the techniques you learn to your own projects.
Follow tutorials on YouTube and Coursera
Expand your knowledge and skills in AI pair programming by exploring video tutorials created by experts.
Show steps
  • Search for tutorials on topics that align with your learning goals.
  • Follow the instructions provided in the tutorials, implementing the code examples.
  • Experiment with different techniques and apply them to your own projects.
Five other activities
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Practice coding exercises on Leetcode
Sharpen your coding skills and familiarize yourself with AI coding patterns commonly asked in AI pair programming interviews.
Show steps
  • Pick a difficulty level and select a set of practice problems.
  • Implement your solutions in your preferred programming language.
  • Compare your solutions to the provided test cases and explore alternative approaches.
Join a study group or participate in online discussions
Connect with fellow learners, share knowledge, and engage in discussions to enhance your understanding of AI pair programming concepts.
Show steps
  • Find or create a study group with other students enrolled in the course.
  • Meet regularly to discuss course materials, solve problems together, and share insights.
  • Participate in online forums and discussion boards to connect with a wider community of learners.
Attend AI pair programming workshops or hackathons
Participate in immersive learning experiences where you can apply AI pair programming techniques in a collaborative environment.
Show steps
  • Search for upcoming AI pair programming workshops or hackathons.
  • Register for the event and prepare by reviewing relevant materials.
  • Attend the event, actively participate in activities, and network with others.
  • Follow up after the event by implementing what you learned in your own projects.
Build a simple AI pair programming tool
Develop hands-on experience in applying AI pair programming concepts by creating a functional tool that assists in coding tasks.
Show steps
  • Identify a specific coding task that you want to automate.
  • Design and implement an algorithm that utilizes AI techniques to solve the task.
  • Create a user interface for your tool that makes it easy to use.
  • Test and refine your tool to ensure accuracy and efficiency.
Contribute to open-source AI pair programming projects
Gain practical experience in AI pair programming by contributing to real-world projects and collaborating with others.
Browse courses on Open Source
Show steps
  • Identify open-source AI pair programming projects on platforms like GitHub.
  • Review the project documentation and identify areas where you can contribute.
  • Fork the project repository and make your changes.
  • Submit a pull request to the original project.

Career center

Learners who complete Pair Programming with a Large Language Model will develop knowledge and skills that may be useful to these careers:

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