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Copy file name to clipboardExpand all lines: ml-zoomcamp-2025/assignments-and-scoring.md
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Assignments are published under `cohorts/2025/{module}` in the GitHub repository of the course. Once ready, the submission form will be shared in Slack.
Announcements are always posted in Slack, Telegram, and the DataTalksClub newsletter. If you don't receive the DataTalksClub newsletter, you may have unsubscribed from it.
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Submissions also appear on the [leaderboard](https://courses.datatalks.club/ml-zoomcamp-2025/leaderboard), which adds a fun competitive element. The leaderboard is optional, but many students find it motivating.
Earning your ML Zoomcamp certificate requires completing a specific combination of projects rather than achieving a particular grade. You must **pass 2 out of 3 available projects**, giving you flexibility in choosing your path. You can complete either the midterm project plus the final capstone project, or the final capstone project plus a second capstone project.
Copy file name to clipboardExpand all lines: ml-zoomcamp-2025/getting-help.md
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Before reaching out for help in Slack, make it a habit to check the comprehensive [FAQ website](https://datatalks.club/faq/machine-learning-zoomcamp.html) first. This resource contains answers to the most common questions from previous cohorts and will often solve your problem immediately.
When you encounter problems, the way you ask for help determines the quality of responses you'll receive. Start by checking the [FAQ website](https://datatalks.club/faq/machine-learning-zoomcamp.html) and searching through previous Slack conversations for similar issues. This preliminary research often reveals that your question has already been addressed.
When you do need to ask for help, provide context that enables others to assist you effectively. Include specific error messages and relevant code snippets (use ```python code blocks), mention your operating system and Python version, and share a link to your GitHub repository so helpers can understand your complete setup. For example, instead of asking "My code doesn't work," provide: "I'm getting `ModuleNotFoundError: No module named 'sklearn'` on macOS with Python 3.11 when running the linear regression notebook from Module 2. Here's my repository: [link]." This approach not only gets you better answers but also helps other students who might face similar challenges.
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Start by visiting the [ML Zoomcamp GitHub repository](https://github.com/DataTalksClub/machine-learning-zoomcamp) and **starring it** ⭐. All course materials are in this repository, with each module having its own folder (for example, `01-intro` or `03-classification`). Cohort-specific homework and deadlines are located in `cohorts/2025`.
The lectures are pre-recorded and available in the [YouTube playlist](https://www.youtube.com/playlist?list=PL3MmuxUbc_hIhxl5Ji8t4O6lPAOpHaCLR), so you can watch them whenever it suits you. Occasionally, additional workshops or updated implementation videos are released—there will be additional announcements if this happens. If nothing is announced, you can assume all necessary materials are already available.
Finally, locate and bookmark the [FAQ Website](https://datatalks.club/faq/machine-learning-zoomcamp.html). This comprehensive resource contains answers to the most frequently asked questions from previous cohorts. Make it a habit to check this document before asking questions in Slack, as your question has likely been answered before.
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Connect with the vibrant ML Zoomcamp community to enhance your learning experience. After registering for the course, you'll receive an invitation to the **Slack Workspace** via email. Join the workspace and navigate to the "machine-learning-zoomcamp" channel, which serves as your primary support and Q&A platform throughout the course.
Consider also joining the **Telegram Channel** for course announcements. While optional, it's highly recommended for receiving important updates and staying connected with course developments.
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The [GitHub repository](https://github.com/DataTalksClub/machine-learning-zoomcamp) contains all course materials and serves as the central hub for the course.
The [course platform](https://courses.datatalks.club/ml-zoomcamp-2025/) is a dedicated platform for submitting homework and accessing course materials.
The [Slack workspace](https://datatalks.club/slack.html) is a community-driven platform for discussing the course and getting help from fellow learners and instructors.
The [Telegram channel](https://datatalks.club/telegram.html) is for course announcements only. If you don't receive the newsletter, you may have unsubscribed from it.
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The [FAQ](https://datatalks.club/faq/machine-learning-zoomcamp.html) is a comprehensive resource containing answers to the most frequently asked questions from previous cohorts. Make it a habit to check this document before asking questions in Slack, as your question has likely been answered before.
Copy file name to clipboardExpand all lines: ml-zoomcamp-2025/success.md
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Share your progress regularly on LinkedIn and Twitter using the #mlzoomcamp hashtag, and tag Alexey Grigorev and DataTalks.Club in your posts. The **leaderboard system** at [courses.datatalks.club/ml-zoomcamp-2025/leaderboard](https://courses.datatalks.club/ml-zoomcamp-2025/leaderboard) awards points for homework completion, FAQ contributions, and resource sharing.
Consider writing blog posts about concepts you've mastered or challenges you've overcome. Examples include "Understanding Cross-Validation in Module 4" or "My Journey Deploying a FastAPI Model." Posts about struggles like "3 Deployment Failures That Taught Me Docker Basics" are often more valuable than success stories. When you feel confident in a topic, answer questions in Slack—teaching others solidifies your own knowledge.
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