Jesper Dramsch

Verified
  • @jesper
  • Machine Learning [PhD & Job] · Data Science [81/100k+] · Creator
  • they
  • Edinburgh, Scotland
 📝 𝗧𝗵𝗲 𝗖𝗹𝗶𝗳𝗳𝗡𝗼𝘁𝗲𝘀: Moin! I'm Jesper, a recovering geophysicist that ventured into machine learning. I...  
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Positions

Scientist for Machine Learning

  • ECMWF
  • Jul 2021 - Present

Skillshare Instructor

  • Skillshare
  • Aug 2020 - Present

Youtube Creator

  • YouTube
  • Apr 2019 - Present

Newsletter Editor

  • Late to the Party
  • Aug 2020 - Present

Machine Learning Engineer

  • GMV NSL
  • Sep 2020 - Jun 2021

Postdoc

  • Technical University of Denmark
  • Nov 2019 - Feb 2020

PhD Candidate

  • Technical University of Denmark
  • Oct 2016 - Nov 2019
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👉 Start here

✨ Awesome McAwesomesauce

10 Highlights

💌 Newsletter

20 Highlights

📢 Soapbox Shenanigans

12 Highlights

🐍 Parselmouth 01100010 01101001 01101110 01100001 01110010 01111001

14 Highlights

🎥 Laterna Magika

5 Highlights

✍️ Digital Scribbles

45 Highlights

👩‍🏫 Edumacation

3 Highlights

🛳️ Atomic Essays

30 Highlights

2021

Nov 23, 2021
Nov 23, 2021
Reposted by Jesper Dramsch
Hosted a podcast
On the season finale of the Software World, I welcome Jesper Dramsch, Scientist for Machine Learning in the European Center for Medium-Range Weather Forecasts (ECMRWF) and Geophysicist.

In our conversation, Jesper talks about the differences between Machine Learning and Data Science, how to enter the field, how life scientists shift their focus to Data Science.

We talk about how businesses should approach data as the processes and methodologies are completely different from software engineering.

Listen to the episode here 👇.

#26: Machine Learning and Data Science with Jesper Dramsch

Nov 08, 2021
Nov 08, 2021
Shipped an Atomic Essay
 Many people think Kaggle is just a competition platform.

This could not be further from the truth. The platform has grown to include amazing learning resources no Machine Learning practitioner should miss out on.

Don't be fooled.

↓ 🧵

🧠 Courses and Certificates on Kaggle Learn

Kaggle has an entire notebook-based course platform, including certificates!

Whether it's #SQL, Feature Engineering, or #MachineLearning Explainability, you can use the integrated compute platform to learn for free.

💾 Find Datasets with Examples

Early steps in data science include obtaining datasets.

Kaggle has gamified uploading data sets to the platform. They have licenses and descriptions of how a dataset was obtained.

Usually, a great starting point to get a free and easy data set.

🏆 Explore Old Competitions to Learn from

I love searching old competitions for new tricks!

Finished competitions very often have a showcase and explanation from the winners detailing how they approached the problem and how they placed in the top ranks!

💻 Find Fantastic Code When it's Hidden from You

The standard sort of Kaggle Code is "Hotness", which makes sense in active competitions.

When you search older code examples, click top right and switch to Most Votes to find the hidden gems from Experts to Grandmasters!

🙊 Don't miss out on Discussions

Especially Grandmasters pushing for that quadruple title
will often info-dump in the discussion section to farm upvotes.

You can essentially get an entire paper review in a single discussion comment.

❌ Don't Miss out on an Invaluable Resource

I have yet to even get close to winning any competition.

Yet, Kaggle has benefited me in more than one way in my professional life.

📚 TL;DR

👉 Get Certificates on @Kaggle Learn
👉 Find Datasets with Examples
👉 Explore old Competitions
👉 Sort Code the Right Way
👉 Don't skip the Discussions

#kaggle #kagglecompetition #education #python #machinelearning #code #deeplearning #learning #datascience #training #career 
Nov 08, 2021
Nov 08, 2021
Wrote Python
Answered @cassidoo's newsletter question.

Given an array of integers, return the index of each local peak in the array. A “peak” element is an element that is greater than its neighbors.


Example:

$ localPeaks([1,2,3,1])
$ [2]

$ localPeaks([1,3,2,3,5,6,4])
$ [1, 5]


Nov 07, 2021
Nov 07, 2021
Shipped an Atomic Essay
You see companies try data science, build a small team and fail.

Apart from the lack of data and often the lack of accessibility to data, expectations can be disappointed

In this essay for #ship30for30, I share there are a few reasons how data science "fails".
Nov 07, 2021
Nov 07, 2021
Shipped an Atomic Essay
Here are 5 lessons I learned from creating daily.

I wrote an essay a day for 30 days and it taught me more and different things than I would have ever expected.

It's the last day of #ship30for30. Is it time for nostalgia yet?
Nov 06, 2021
Nov 06, 2021
Shipped an Atomic Essay
You heard that scientists make great scientists?
But how do you actually translate your academic experience into a resume that gets you jobs?

In this essay for #ship30for30, I share how to translate common scientific experience into a top resume.

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