This is the repository for the LinkedIn Learning course Maximize Your Claude Code Programming Productivity. The full course is available from LinkedIn Learning.
See the readme file in the main branch for updated instructions and information.
Claude Code is exploding across the development landscape. What began as a model optimized for developers has sprouted an ever-expanding set of tools for creating, managing, integrating, and deploying software. Learn to plan and implement sophisticated, AI-driven workflows that go beyond basic code prompting. Discover the essential skills to set up an optimized UI design system, effortlessly incorporating Figma designs into your projects. Hone your skills in test-driven development to ensure code quality and robustness in any development pipeline. Delve into developer-focused environments, setting up MCP servers for seamless integration across platforms like GitHub and Jira. Elevate your coding with the creation of automated, intelligent agents designed to enhance your development ecosystem. Perfect for developers already familiar with Claude Code, this course is tailored to boost productivity and innovation within your existing workflows.
- Design structured AI workflows using multistep plans, Skills, MCP servers, and managed agents to move beyond simple prompting.
- Apply AI‑driven testing and quality practices including TDD, unit, integration, smoke, and automated UAT in modern development pipelines.
- Create production-ready front‑end and product experiences by integrating designs, component libraries, and PRDs with Claude Code.
- Integrate Claude Code across the developer toolchain by connecting GitHub, Jira, data platforms, observability tools, and documentation systems.
- Adapt to the evolving role of developers by leveraging AI agents for coding, data analysis, reporting, and decision-making at scale.
This repository has branches for each of the videos in the course. You can use the branch pop up menu in github to switch to a specific branch and take a look at the course at that stage, or you can add /tree/BRANCH_NAME to the URL to go to the branch you want to access.
The branches are structured to correspond to the videos in the course. The naming convention is CHAPTER#_MOVIE#. As an example, the branch named 02_03 corresponds to the second chapter and the third video in that chapter.
Some branches will have a beginning and an end state. These are marked with the letters b for "beginning" and e for "end". The b branch contains the code as it is at the beginning of the movie. The e branch contains the code as it is at the end of the movie. The main branch holds the final state of the code when in the course.
When switching from one exercise files branch to the next after making changes to the files, you may get a message like this:
error: Your local changes to the following files would be overwritten by checkout: [files]
Please commit your changes or stash them before you switch branches.
Aborting
To resolve this issue:
Add changes to git using this command: git add .
Commit changes using this command: git commit -m "some message"
Denys Linkov Head of ML at Wisedocs
Denys leads efforts in natural language understanding, generative natural language processing, MLOps, and conversational data discovery and augmentation. Denys helps build thriving, sustainable tech communities, with a focus on youth development and the future of work. Denys has a bachelor of science in computer science from the University of Toronto, has worked with a number of youth groups as a mentor and speaker, and helped scale TU20, a leading youth tech group in Halton, Ontario.
Check out my other courses on LinkedIn Learning.