Jun 2, 2023

Getting Started with AI/ML

Learn how to get started with AI/ML and become a builder. Discover tips for the first month, including learning to use notebooks, dealing with data, and building your first project. Get ready to revolutionize the world!

AI MACHINE_LEARNING TECHNOLOGY

Santiago

Machine Learning. I run https://t.co/iZifcK7n47 and write @0xbnomial.

Member of Software Developers

AI will revolutionize the world in the next 3 to 5 years.

But we need more builders: people willing to work and learn solid AI/ML skills.

Here is how you can start:
— Santiago (@svpino) June 2, 2023
Most people think that starting is hard. They are wrong.

These recommendations will get you through the first month.

1. Learn to use notebooks
2. Learn to deal with data
3. Learn data visualization
4. Learn basic algorithms
5. Build your first project

Here are a few resources:
— Santiago (@svpino) June 2, 2023
But first, let me thank the sponsor for today's post:

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— Santiago (@svpino) June 2, 2023
For this guide to be helpful, you need to know Python.

If you are comfortable writing Python, keep going. If you aren't, I'd suggest you start there.
— Santiago (@svpino) June 2, 2023
1. Learn to use notebooks

You want to learn about notebooks: Jupyter or Google Colab are your friends.

Notebooks are a fantastic way to code, experiment, and communicate your results.

Here is a 30-minute tutorial on Jupyter Notebooks:https://t.co/j48sLNXBXo pic.twitter.com/j5H8aGOZLi
— Santiago (@svpino) June 2, 2023
2. Learn to deal with data

Pandas is a one-stop shop for this.

"10 minutes to pandas" is an excellent tutorial to get you started on the basics: https://t.co/NL9c0aGa9t.

Also, watch this video: https://t.co/dtnTzOSqSu. pic.twitter.com/gxS99Eekp1
— Santiago (@svpino) June 2, 2023
3. Learn data visualization

It's critical to learn data visualization and how to showcase the work you are doing.

Kaggle's tutorial is a fantastic start: https://t.co/V0ya8kLMsu

Also, watch this 6-minute tutorial on Seaborn: https://t.co/eAU5NBuK0U. pic.twitter.com/nVHpemfKVT
— Santiago (@svpino) June 2, 2023
4. Learn basic algorithms

A few suggestions: Decision Trees, KNN, Linear Regression, and Neural Networks.

Kick it off with the Machine Learning Recipes from Google: https://t.co/dgx97kJkVg pic.twitter.com/76uNIluKsf
— Santiago (@svpino) June 2, 2023
Before talking about your first project, it's time to go through an end-to-end tutorial that will put everything together for you.

Look at the "Intro to Machine Learning" tutorial.

It's a quick tutorial that will bring together all of the pieces:https://t.co/qQXBcdvVhR pic.twitter.com/zKasxYUhnA
— Santiago (@svpino) June 2, 2023
5. Build your first project

The tutorial from the previous step ends with the Titanic exercise. You can find it on Kaggle: https://t.co/v2TDFx1aqA.

This is everything you need to get started and finish your first Machine Learning project! pic.twitter.com/CD2Z6R4cTm
— Santiago (@svpino) June 2, 2023