The perfect way to get into machine learning is to find an algorithm that improves your work right away without much drama.

For software developers, KNN (K-Nearest Neighbors) is a perfect introduction:

▫️ Surprisingly familiar
▫️ Powerful enough

🧵👇

I also like Decision Trees for software developers, but if you did that already, look into KNN.

Here is the summary you need to know: KNN is like a fancy search algorithm that will help you do cool things.

2/6

Here are some of the things that you could solve using KNN:

1. If you have a dataset with missing values, you can use KNN to find a good value for them.

2. You can build a simple recommendation system to promote related products in your store using KNN.

3/6

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Santiago

@svpino

26 Mar

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

Read 5 tweets

Santiago

@svpino

25 Mar

@awscloud

A summary of the setup I use for work and how I use each one of these:

▫️ Google Colab PRO
▫️ @awscloud's SageMaker
▫️ Mac Pro running @code

Here are the details: 🧵👇

I always start new things with Google Colab.

Opening Colab is fast, and I can go from an idea to a running script in no time.

I can share the notebook and open it anywhere without worrying about version control systems.

2/9

Any new experiments go into Colab. Whenever I need to test something, I do it in Colab.

Paying for the PRO version is a no-brainer for me:

▫️ Faster GPUs
▫️ More RAM
▫️ More Disk
▫️ Longer runtime

3/9

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Santiago

@svpino

25 Mar

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.

https://twitter.com/EngFod/status/1375016779068674050?s=20

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.

https://twitter.com/EngFod/status/1375016779068674050?s=20

Read 6 tweets

Santiago

@svpino

24 Mar

🐍 Python 3 features that you might not be using yet:

▫️ Type hints
▫️ Data classes
▫️ Pathlib
▫️ Enumerations
▫️ F-strings
▫️ Iterable unpacking
▫️ Walrus operator
▫️ Async IO
▫️ Assignment expressions
▫️ Positional-only parameters

Pick one and see how it can help you.

I like to spend some time every week looking into something new from Python 🐍.

2 out of 3 times, I can't use it right away. I don't find a good way to make it work for me.

I usually talk about what I learned here on Twitter and then put it in the backburner.

Sometimes, I find a good place right away for what I just learned, and there's no better feeling than that!

I think people need more Python 🐍 in their lives:

- Simple
- Popular
- Powerful
- Versatile

Follow me and I'll make sure we learn this thing together until it hurts.

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