ML Zoomcamp announcements

We published the public top 100 leaderboard https://github.com/DataTalksClub/machine-learning-zoomcamp/blob/master/cohorts/2023/leaderboard.md thanks everyone for taking part in the course!

Hi everyone! We've scored the remaining things (competition and articles) and updated the leaderboard. https://docs.google.com/spreadsheets/d/e/2PACX-1vSNK_yGtELX1RJK1SSRl4xiUbD0XZMYS6uwHnybc7Mql-WMnMgO7hHSu59w-1cE7FeFZjkopbh684UE/pubhtml The folks from the top-100 of the leaderboard should have received an email with the link to the form (the same as posted above) Please fill this form in by the end of this week, and we will create a public leaderboard after that Thanks everyone for taking part in the course!

Three more things left before we completely wrap up the course for this year First, if you wrote an article, but haven't shared it yet, please do it as soon as possible. You'll find the link to the form here: https://github.com/DataTalksClub/machine-learning-zoomcamp/blob/master/cohorts/2023/article.md Second, if you took part in the competition, make sure you submit your team name here: https://forms.gle/S3bSj7TSKVAcDfjPA Teams with names machffud, шайлушай and Please do it till Thursday EOD, so on Friday we can start collecting the data for the public top-100 leaderboard. To make this process easier, if you think you'll be one of the top 100, you can already put your data here: https://forms.gle/DLTULsG2BosoGL7K9

The course has officially ended! 178 people graduated from the course. You can access your certificate using this instruction: https://github.com/DataTalksClub/machine-learning-zoomcamp/blob/master/cerficates.md The leaderboard is updated with information about the capstone 2 project. You'll find the link to the feedback in the projects section of the 2023 cohort Congratulations to everyone! There will be a few more updates to the leaderboard - we still need to add the articles, the competition. After that we will start collecting the data for the top-100 leaderboard

Great work on the projects! Now it's time to evaluate your peers and learn from them You'll find all the links here: https://github.com/DataTalksClub/machine-learning-zoomcamp/blob/master/cohorts/2023/projects.md#capstone-2 The evaluation is due in one week - you have time till next Thursday 23:00 CET. Have fun! Also don't forget about the competition and the article

We're extending the deadline for project submissions till Thursday, 23:00 CET. Use this time wisely. Have fun!

How is it going with your projects? Everyone on track?

Also, we have great news! NVidia is giving us a GPU (RTX 4080) as the prize for the competition! A few conditions: - Your solution must not use any leaks or any other information that is not available during prediction time - It should be possible to send you a GPU. So if you live in a country under sanctions, we won't be able to give you the prize How is it going with the competition? Having fun?

Hi everyone! Great work on capstone 1 projects! We have scored the projects, and the leaderboard is updated: https://docs.google.com/spreadsheets/d/e/2PACX-1vSNK_yGtELX1RJK1SSRl4xiUbD0XZMYS6uwHnybc7Mql-WMnMgO7hHSu59w-1cE7FeFZjkopbh684UE/pubhtml Feedback from your peers: https://docs.google.com/spreadsheets/d/e/2PACX-1vQMT0Pr_EYLGR_8dXQ384cVkrN2w73VNr_H-mgsgIV2XDjKHrm0cznHaDGaWUyI146sx8-AkBL1QFrF/pubhtml?gid=1337692157&single=true Congrats to everyone who passed! If you're doing capstone 2 project, here's the link for submitting it: https://forms.gle/MMj6W3JB4sobUajN9

Hi everyone! Happy new year! How is it going? Here are a few things we want to announce First, you can write an article (optionally) as a part of this course where you can explore any topic that wasn't covered in the course. The best way to learn something is to teach it! You can read more about the articles here: https://github.com/DataTalksClub/machine-learning-zoomcamp/tree/master/article Links for submitting your articles: https://github.com/DataTalksClub/machine-learning-zoomcamp/blob/master/cohorts/2023/article.md (there's a form) Second, if you're taking part in the competition, you can also share what you learned. Here's the link for submitting your results and learning in public links: https://forms.gle/S3bSj7TSKVAcDfjPA (you can find this link at the bottom of the 2023 cohort page)

Hi everyone! Great work on the projects! Now it's time to learn from your peers and evaluate their projects You can find project assignments here: https://github.com/DataTalksClub/machine-learning-zoomcamp/blob/master/cohorts/2023/projects.md#capstone-1 Don't forget that if you don't do evaluations, you fail your project Happy learning!

Today is the deadline for submitting the project. Don't forget to make your GitHub repo public!

Hi everyone! We're extending the deadline for submitting your capstone 1 projects by two days. The new deadline is Wednesday, December 20, 23:00 CET. Use this time wisely. Have fun!

Timur has prepared a getting started notebook for the competition It uses BERT. This is how it works: - You get embeddings for questions and answers using BERT (embeddings are vector representations of texts) - For each question and the answer candidate, you calculate the cosine similarity and then select the best candidate as the final answer You can access the notebook here: https://github.com/DataTalksClub/kaggle-qa-challenge-starter Have fun!

We have updated the leaderboard: scored homework #9 and #10 https://docs.google.com/spreadsheets/d/e/2PACX-1vSNK_yGtELX1RJK1SSRl4xiUbD0XZMYS6uwHnybc7Mql-WMnMgO7hHSu59w-1cE7FeFZjkopbh684UE/pubhtml We had 193 and 134 submissions respectively Great job!

We're almost done with the course lectures, but there's still a lot to learn! Today we're starting the Capstone 1 project. The process for capstone is the same as for the midterm project You can find the link to the form for submitting the project here: https://github.com/DataTalksClub/machine-learning-zoomcamp/blob/master/cohorts/2023/projects.md#capstone-1 Also, we extend the deadline for the Kubernetes homework by two days. And finally, there's another optional module about KServe - a tool on top of Kubernetes to make it easier to serve ML models. You can find it here: https://github.com/DataTalksClub/machine-learning-zoomcamp/tree/master/11-kserve There's no homework for module 11 Have fun!

We don't cover NLP in the course, but what's more fun than learning about it yourself? To help you do that, we came up with the Q&A challenge - this is a kaggle competition where you can build a model for selecting the most relevant answer for a question https://www.kaggle.com/competitions/dtc-zoomcamp-qa-challenge/overview In this competition, we used the Q&A data from our office hours. Using external data is welcome (check the "Data" tab for more information and ideas) Participants will get course points, but we're also working on getting more prizes. For example, a GPU from NVidia, like last year. Don't forget that you can use Saturn Cloud for taking part in the competition: https://bit.ly/saturn-mlzoomcamp Now we're preparing on a few getting started notebooks, so stay tuned if you have no clue how to start The competition finishes on February 1. Have fun!

We're starting the Kubernetes module! Materials: https://github.com/DataTalksClub/machine-learning-zoomcamp/tree/master/10-kubernetes Homework: https://github.com/DataTalksClub/machine-learning-zoomcamp/blob/master/cohorts/2023/10-kubernetes/homework.md Have fun! We're almost done with the lectures =) Also, we extend the deadline for the serverless homework by 2 days. The new deadline is Wednesday, November 29, 23:00 CET

Hi everyone! We scored homework #8 Solution notebook: https://github.com/DataTalksClub/machine-learning-zoomcamp/blob/master/cohorts/2023/08-deep-learning/homework.ipynb Updated leaderboard: https://docs.google.com/spreadsheets/d/e/2PACX-1vSNK_yGtELX1RJK1SSRl4xiUbD0XZMYS6uwHnybc7Mql-WMnMgO7hHSu59w-1cE7FeFZjkopbh684UE/pubhtml We had 211 submissions. That's great. Keep going!

Deep learning is fun, but so is knowing how to deploy the models Today we start module #8 where we will see how to deploy TensorFlow models with TF-Lite and AWS Lambda Materials: https://github.com/DataTalksClub/machine-learning-zoomcamp/tree/master/09-serverless Homework: https://github.com/DataTalksClub/machine-learning-zoomcamp/blob/master/cohorts/2023/09-serverless/homework.md You will need access to AWS to do the actual deployment, but if you can't use AWS, most of the code can run locally with Docker. You don't need AWS for the homework Have fun! And also, we extend the deadline for the deep learning homework by 2 days - now it's Wednesday, 22 November 23:00 CET