I've been talking about machine learning for a while now.
It has taken me some time to understand who is my audience, and—more importantly—, who do I want to speak to.
1/5
I want my content to be driven by what excites me. That's the only way I can ensure I'll stay engaged and the content will have a high quality.
I listen to what people want to shape my ideas but always prioritize what I want to say.
2/5
Here is the persona that I want to talk to:
"You are a software developer interested in incorporating machine learning into your tool set. You might be starting from scratch or be on your way, but you aren't an expert yet ... →
3/5
← ... You are curious, and you hope machine learning is the next step in your career. You are looking for somebody to crack the field open for you and get you in a path from where you can take off."
4/5
If this sounds like you, then yes, you are the person I'm trying to talk to. Everyone else will still benefit, but you will get the most out of it.
And the best possible way we can make this happen is over email: digest.underfitted.io. I'll see you there!
5/5
• • •
Missing some Tweet in this thread? You can try to
force a refresh
▫️ Better career opportunities
▫️ Pays really well
▫️ Rapid growth
▫️ It's shaping the future
▫️ Creativity over repetition
Most importantly, it gives us access to solve problems that we wouldn't be able to crack without.
You might not have focused on it yet, but it's not as far from you as you may think.
Here is my recommendation: start reading about it a little bit. You don't have to make any world-rocking changes, just inform yourself better and see what happens.
This depends on your country and the opportunities that exist around you. That being said, conventional development jobs will continue to be more popular.
But every day, there will be more machine learning jobs. The demand will continue increasing.
If you are starting out with machine learning, these algorithms will give you the best bang for your money:
▫️ Decision Trees
▫️ Linear Regression
▫️ Logistic Regression
▫️ Random Forest
▫️ AdaBoost
▫️ Naive Bayes
▫️ KNN
▫️ Neural Networks
▫️ K-means
▫️ PCA
If you are looking to make things a little bit more practical, XGBoost will solve a lot of your problems.
I didn’t include it in the previous list because it’s a combination of Decision Trees with Bagging and Boosting, but it’s definitely one of algorithms that I use the most.
Information overload is a real problem. If you do a Google search, there are literally thousands of machine learning algorithms.
This list will keep you focused on the list that will give you the most benefits when you are starting.