/machine-learning-zoomcamp

Learn ML engineering for free in 4 months!

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Machine Learning Zoomcamp

Syllabus

Taking the course

2024 cohort

We start the course again in September 2024

Self-paced mode

You can take the course at your own pace. All the materials are freely available, and you can start learning at any time.

To take the best out of this course, we recommened this:

  • Register at DataTalks.Club and join the #course-ml-zoomcamp channel
  • For each module, watch the videos and work through the code
  • If you have any questions, ask them in the #course-ml-zoomcamp channel in Slack
  • Do homework. There are solutions, but we advise to first attempt the homework yourself, and after that check the solutions
  • Do at least one project. Two is better. Only this way you can make sure you're really learning. If you need feedback, use the #course-ml-zoomcamp channel

Of course, you can take each module independently.

Prerequisites

  • Prior programming experience (at least 1+ year)
  • Being comfortable with command line
  • No prior exposure to machine learning is required

Nice to have but not mandatory

  • Python (but you can learn it during the course)
  • Prior exposure to linear algebra will be helpful (e.g. you studied it in college but forgot)

Asking questions

The best way to get support is to use DataTalks.Club's Slack. Join the #course-ml-zoomcamp channel.

To make discussions in Slack more organized:

We encourage Learning in Public

Putting everything we've learned so far in practice!

For the deep learning part, we need to use a GPU. ML Zoomcamp students can use Saturn Cloud and get extra 150 GPU hours there. Message support and say "I'm enrolled in ML Zoomcamp" to get an upgrade.

11. KServe (optional)

Putting everything we've learned so far in practice one more time!

Writing an article about something not covered in the course.

For those who love projects!

If you liked our deep learning module, join us to build a model for classifying cups, glasses, plates, spoons, forks and knives.

Submit your learning in public links here

Previous cohorts

2021 Cohort

2022 Cohort

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