Thread by @paulabartabajo_

3 years ago I struggled to land my first freelance ML engineering contract.

Then I discovered this ↓

Building one professional real-world ML project is the best way to stand out from the crowd, and land an ML job.

Here is what I did, 𝘀𝘁𝗲𝗽-𝗯𝘆-𝘀𝘁𝗲𝗽 👩‍💻👨🏽‍💻↓

Step 1. Find a real-world problem you are interested in

Working on projects is harder than completing online courses.
But hey, no pain no gain.

It is VERY important you work on a problem you are interested in.
Otherwise, you will quit.

Step 2. Find a data source

Preferably a live API. If not possible, pick a static dataset from Kaggle.

Here is a superb repo with a list of public APIs you can use
github.com/public-apis/pu…

Step 3. Build a simple ML model

Do not try to build THE PERFECT model, and only then move to the next phase.

Because this leads you to a never-ending Jupyter-notebook-development-cycle, and you get lost.

Start with basic features and a basic model.

And move to the next step.

Step 4. Build a Minimum Viable Product

A Jupyter notebook is not enough to prove your solution might work.
You need to go one step further and build a minimal working system.

I recommend you follow the 3-pipeline design ↓
datamachines.xyz/2023/03/27/rea…

Step 5. Start iterating on the model

Once the system works, start improving it by
- increasing training data size
- increasing the number of features
- trying a more complex ML model
- optimizing model hyper-parameters

Step 6. Push your code to a public GitHub repo and write a beautiful README

The README file is the first thing your future employer will se.

Explain the problem you wanted to solve, and the solution you built.

Here is an example
github.com/Paulescu/bytew…

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