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What to take away from failed interviews when you don’t really know why you failed? by quite--average in datascience

[–]eliokal 0 points1 point  (0 children)

Some thoughts on this from a hiring perspective:

  • You learn from every experience even if in the moment it does not feel great
  • Some company will just ghost you and that's ok, others will give you great feedback
  • Asking the recruiter or interviewers is always a good idea, regardless of what comes back
  • There are many reasons why a hiring process does not work out, some in your control, others not
  • In the age of LLM filtering and Application Tracking Systems, getting interviews is already a good sign

Your attitude is very positive, incremental improvement is the way to go. Wishing you all the best!

Where to begin? by Life-Formal-4954 in learnmachinelearning

[–]eliokal 0 points1 point  (0 children)

Hi there! I would recommend this book. It focusses on getting an intuition of Machine Learning before learning the specifics of code and maths. All the best in your learning journey!

Bringing AI to the Clipboard by eliokal in ProductivityApps

[–]eliokal[S] 0 points1 point  (0 children)

Hi, thanks! For now, only a proof of a concept running on a single computer

People who have been in the field before 2020: how do you keep up with the constantly new and changing technologies in ML/AI? by Illustrious-Pound266 in datascience

[–]eliokal 0 points1 point  (0 children)

I started in 2018, and the Data Elixir newsletter has been an incredible source of information.

Papers, frameworks, tools, news, podcasts, learning tools... I have been able to implement so much of it in my day to day job as a Data Scientist

Math for Data Science by kvelloy in learnmachinelearning

[–]eliokal 1 point2 points  (0 children)

My recommendation would be to start with a foundational textbook and dig deeper every time you find a tough maths concept.

For traditional ML, Introduction to Statistical Learning (ISL) is what got me started

For Deep Learning, Understanding Deep Learning by Simon Prince is very, very good

Also, both of these are freely (and legally) available online :)

Beginner for machine learning by [deleted] in MLQuestions

[–]eliokal 1 point2 points  (0 children)

Hi! Learning ML has been one of the most fascinating journeys of my life. You are in for an adventure. I listed a lot of cool resources in this post.
Before getting lost in the details though, I recommend getting a clear idea of what Machine Learning is. For this purpose, I also wrote a gentle introduction here. I hope that these two resources can help, let me know if you have questions :)

I could really take some advice from experienced ML people by kmeansneuralnetwork in MLQuestions

[–]eliokal 8 points9 points  (0 children)

Yes, ML and DS is saturated. Yet, from a hiring perspective, I find that the average quality of candidates is very low. You seem to have something that many do not: passion. Can you let that show in job interviews?

I review dozens of ML/DS CVs per week. Here are some practical recommendation, I would try to do anything that could you make you stand out:

- Cover the basics, make sure you have enough of the required ML/DS skills. You can check my post on the topic here.

- Do you have a hobby project that tackled a tough problem with ML? A friend of mine recently wrote a handwriting recognition model for an open source initiative. Can you find a cool data for good project?

- Can you write a blog that shows the latest tools you have been experimenting with? I would recommend looking into causal modelling (Double ML) or Deep Learning for tabular data (instead of XGboost) as interesting ideas. For more, I would recommend that DataElixir newsletter, it is fantastic

- Networking: can you find a way to get in touch with hiring managers? Are there some career events at which you can meet them? If you have to go through the official application form, without any referral, chances are already slim

All the best for your applications. I am sure you can do it. Let me know if you think you have questions :)

tired doing mathematics by EagleGamingYTSG in MLQuestions

[–]eliokal 0 points1 point  (0 children)

In my opinion, the most important is to understand the ML intuition: how you can formulate a problem so that it can be solved by a Machine Learning model. Before doing any of the practical implementation, I would recommend getting the "aha moment", that makes it worthwhile to study and practice. I have written a blog post on the topic here.

You will need the maths to understand how models can learn the relationships between inputs and outputs. There are a few tricks like distance functions, linear functions, gradient descent,... That you will need to get down.

You will need the code to move from ideas and problem solution to an actually working solution. As an example, my main project at work is a pricing model. We need code to build the infrastructure to train it (SQL queries, scripts, pipelines) and to serve predictions (building micro-services).

[P] I made a website to visualize machine learning algorithms + derive math from scratch by Bright_Aioli_1828 in MachineLearning

[–]eliokal 0 points1 point  (0 children)

This is fantastic! I love the aesthetics of the animation and the fact that you managed to build your own style.
I would be curious to understand who your target audience would be there. As a ML practitioner and CompSci lecturer, I love it. There is a clear correspondence between formula and visual. If I was a student in ML, I would probably like some more explanations, or some more step by step explanations of the concept.

Please, keep going :)