3 years ago I struggled to land my first freelance ML engineering contract.
Then I discovered this β x.com
Then I discovered this β x.com
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, πππ²π½-π―π-πππ²π½ π©βπ»π¨π½βπ»β
Here is what I did, πππ²π½-π―π-πππ²π½ π©βπ»π¨π½βπ»β
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.
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
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
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.
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 β
datamachines.xyz
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 β
datamachines.xyz
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
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
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
Wanna learn step-by-step, hands-on, how to transform a real-world business problem into a fully working ML solution?
Get lifetime access to the Real-World ML Tutorial + Community today and learn the skills that stand you out from the crowd
realworldmachinelearning.carrd.co
Get lifetime access to the Real-World ML Tutorial + Community today and learn the skills that stand you out from the crowd
realworldmachinelearning.carrd.co
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Wanna help?
β Like/Retweet the first tweet below to spread the wisdom βββ
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