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Quick Guide & Tips

💻   Accessing Utils File and Helper Functions

In each notebook on the top menu:

1:   Click on "File"

2:   Then, click on "Open"

You will be able to see all the notebook files for the lesson, including any helper functions used in the notebook on the left sidebar. See the following image for the steps above.


💻   Downloading Notebooks

In each notebook on the top menu:

1:   Click on "File"

2:   Then, click on "Download as"

3:   Then, click on "Notebook (.ipynb)"


💻   Uploading Your Files

After following the steps shown in the previous section ("File" => "Open"), then click on "Upload" button to upload your files.


📗   See Your Progress

Once you enroll in this course—or any other short course on the DeepLearning.AI platform—and open it, you can click on 'My Learning' at the top right corner of the desktop view. There, you will be able to see all the short courses you have enrolled in and your progress in each one.

Additionally, your progress in each short course is displayed at the bottom-left corner of the learning page for each course (desktop view).


📱   Features to Use

🎞   Adjust Video Speed: Click on the gear icon (⚙) on the video and then from the Speed option, choose your desired video speed.

🗣   Captions (English and Spanish): Click on the gear icon (⚙) on the video and then from the Captions option, choose to see the captions either in English or Spanish.

🔅   Video Quality: If you do not have access to high-speed internet, click on the gear icon (⚙) on the video and then from Quality, choose the quality that works the best for your Internet speed.

🖥   Picture in Picture (PiP): This feature allows you to continue watching the video when you switch to another browser tab or window. Click on the small rectangle shape on the video to go to PiP mode.

√   Hide and Unhide Lesson Navigation Menu: If you do not have a large screen, you may click on the small hamburger icon beside the title of the course to hide the left-side navigation menu. You can then unhide it by clicking on the same icon again.


🧑   Efficient Learning Tips

The following tips can help you have an efficient learning experience with this short course and other courses.

🧑   Create a Dedicated Study Space: Establish a quiet, organized workspace free from distractions. A dedicated learning environment can significantly improve concentration and overall learning efficiency.

📅   Develop a Consistent Learning Schedule: Consistency is key to learning. Set out specific times in your day for study and make it a routine. Consistent study times help build a habit and improve information retention.

Tip: Set a recurring event and reminder in your calendar, with clear action items, to get regular notifications about your study plans and goals.

☕   Take Regular Breaks: Include short breaks in your study sessions. The Pomodoro Technique, which involves studying for 25 minutes followed by a 5-minute break, can be particularly effective.

💬   Engage with the Community: Participate in forums, discussions, and group activities. Engaging with peers can provide additional insights, create a sense of community, and make learning more enjoyable.

✍   Practice Active Learning: Don't just read or run notebooks or watch the material. Engage actively by taking notes, summarizing what you learn, teaching the concept to someone else, or applying the knowledge in your practical projects.


📚   Enroll in Other Short Courses

Keep learning by enrolling in other short courses. We add new short courses regularly. Visit DeepLearning.AI Short Courses page to see our latest courses and begin learning new topics. 👇

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🙂   Let Us Know What You Think

Your feedback helps us know what you liked and didn't like about the course. We read all your feedback and use them to improve this course and future courses. Please submit your feedback by clicking on "Course Feedback" option at the bottom of the lessons list menu (desktop view).

Also, you are more than welcome to join our community 👉👉 🔗 DeepLearning.AI Forum


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Welcome to Claude Code, the Highly Agentic Coding Assistant. This short course is built in partnership with Anthropic and Anthropic's Elie Schoppik is back to share with us best practices for how to use Claude Code. I'm really excited about this short course. Claude Code's my personal favorite coding assistant right now, and it has boosted my and many other developers' productivity by a large margin. And it is a tool with a lot of depth to it. And so I want to get together with Anthropic, to teach, hopefully the definitive course on all of the most important ideas behind how to use it in a systematic way. Thanks, Andrew. I'm excited to be back here and start like you mentioned from explaining what the tool is, how it works, all the way towards using it in parallel with many different tools including Git worktrees and MCP servers. What we've seen over the last couple years is AI assisted coding has evolved rapidly. It started from maybe people asking LLMs occasional coding questions. suggestions to then GitHub autocomplete to then, your various tools that became more and more autonomous. And Claude Code, when it was released, was definitely a step up in terms of the degree of agency or the amount of stuff that the coding assistant could do by itself. And so I think many people were surprised that you could set a task that Claude would work on for many minutes or sometimes even more than a few minutes. And now there are developers that are orchestrating not just a single Claude instance, but even several of them. working in parallel on different parts of a codebase. But coordinating all this has to set the best practices that is not widely known and if you have not worked with people close to the best practices, I think there could be a big uplift still for mastering these best practices and knowing how they drive that amazing productivity that I'm seeing many developers have using Claude Code. So as we start to talk about those best practices, a key tip for working with Claude Code is providing clear context to help Claude code achieve the task you want efficiently. This means pointing Claude code to the relevant files, clearly describing the features and functionality that you want and making sure that you're properly extending the capabilities of Claude code with MCP servers and other tools in that ecosystem. In this course, you'll apply those best practices to three different examples. We'll start with a RAG chatbot and you'll implement the features from the front end to the back end, including refactoring code, writing tests, and then using the GitHub integration to work with pull requests and fixing issues. You'll make use of many Claude Code features like planning, thinking modes, creating parallel sessions, and managing Claude's memory. For the second example, we'll shift gears and work with Jupyter notebooks to explore e-commerce data. We'll refactor notebooks using Claude Code, remove redundant code, and create powerful dashboards with web applications. Finally, we'll move to create a visual mockup based in Figma and use Claude Code, the Figma MCP server and a different MCP server to import the design, iterate, test, and build agentically a powerful front-end application. If you're not currently a Claude Code user, I think learning this set of ideas will give you a meaningful acceleration in the way that which you can engineer systems. And even if you are a current Claude Code user, I think having Ellie share these best practices with you in a comprehensive and systematic way, will, I hope, leave you with quite a few new things you try that will be useful. your work. I'd like to thank from DeepLearning.AI, Hawraa Salami, who had contributed to this course. In the next video, Elie will share Claude Code's underlying architecture, and you might be surprised by how simple the architecture is. Claude Code relies on just a small number of tools to search for patterns within your code files, to list directories, look at files, do regex. It does not rely on semantically embedding your code in the code base or transforming it into searchable structure. And one of the things that I think has made Claude Code effective is how it agentically can read through your code to take notes in a file called code.md to figure out autonomously what is going on your codebase to then drive decision-making on how to advance your code. That's right. And because of that, and not having a need to index the codebase, you can ensure the codebase stays local. We'll talk about some of the security ramifications with that. So let's get started. And I'll see you in the next video.
course detail
Next Lesson
Claude Code: A Highly Agentic Coding Assistant
  • Introduction
    Video
    ・
    4 mins
  • What is Claude Code?
    Video
    ・
    8 mins
  • Course Notes
    Reading
    ・
    1 min
  • Setup & Codebase Understanding
    Video
    ・
    14 mins
  • Adding Features
    Video
    ・
    17 mins
  • Testing, Error Debugging and Code Refactoring
    Video
    ・
    12 mins
  • Adding Multiple Features Simultaneously
    Video
    ・
    11 mins
  • Exploring Github Integration & Hooks
    Video
    ・
    10 mins
  • Refactoring a Jupyter Notebook & Creating a Dashboard
    Video
    ・
    12 mins
  • Creating Web App based on a Figma Mockup
    Video
    ・
    9 mins
  • Conclusion
    Video
    ・
    1 min
  • Prompts & Summaries of Lessons
    Reading
    ・
    10 mins
  • Course Feedback
  • Community