> For the complete documentation index, see [llms.txt](https://opencampus.gitbook.io/opencampus-machine-learning-program/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://opencampus.gitbook.io/opencampus-machine-learning-program/courses/archive/python-from-beginner-to-practitioner/week-6.md). # Week 6 ### To-Do Homework: * Do days 12 & 13 of the course * Watch the videos * Do the interactive coding exercises (Replit & Auditorium) * Watch the videos for day 15 for explanation of the terms IDE and Python Environment * **We will use a different setup method:** * Download and Install the Anaconda Distribution: * * You should be able to launch the Anaconda Navigator locally * We will continue setup in the next session (instructions below) *** ### Local Setup Instructions * Install Anaconda Distribution and Visual Studio Code * & * Mac users: Move from Downloads to Applications to install VS Code system-wide * Launch Anaconda Navigator * Under Environments create a new environment, e.g. *python\_course*, with a recent version of python (> 3.10) * In the package listing for the environment change filter from „installed“ to „all“ then search and install the package *ipykernel* * Under Home launch VS Code (if not available launch it from system) * In VS Code: * Install the extensions *Python* and *Jupyter* (both signed by Microsoft) * Create a new file: File > New File > Jupyter Notebook * Select Kernel > Another Kernel > Python Environments > *python\_course* or whichever environment you just created * Start Coding! *** ### Totally Optional * **Watch lecture 6 of the Havard Python Course:** {% embed url="" %} For the best learning experience, it's recommended to watch the lecture twice: first for a quick overview, and then more slowly while taking notes. Please ensure you allocate sufficient time for this. * **Work through the following homework assignment** * Always first create a copy with "Copy to Drive" or download the notebooks to work locally on them. Otherwise your progress won't be saved!! --- # Agent Instructions This documentation is published with GitBook. GitBook is the documentation platform designed so that both humans and AI agents can read, navigate, and reason over technical content effectively. Learn more at gitbook.com. ## Querying This Documentation If you need additional information that is not directly available in this page, you can query the documentation dynamically by asking a question. Perform an HTTP GET request on the current page URL with the `ask` query parameter, and the optional `goal` query parameter: ``` GET https://opencampus.gitbook.io/opencampus-machine-learning-program/courses/archive/python-from-beginner-to-practitioner/week-6.md?ask=&goal= ``` `ask` is the immediate question: it should be specific, self-contained, and written in natural language. `goal` is optional and describes the broader end goal you are ultimately trying to accomplish on behalf of the user. GitBook uses it to tailor the answer towards what is most useful for that goal. The response will contain a direct answer to the question and relevant excerpts and sources from the documentation. Use this mechanism when the answer is not explicitly present in the current page, you need clarification or additional context, or you want to retrieve related documentation sections.