Santiago Profile picture
2 Dec, 9 tweets, 2 min read
The HTML + CSS Twitter conspiracy.

A tread 🧵👇
A lot of people out there recommend starting with HTML and CSS to aspiring developers.

They suggest this combination is a stepping stone for you to reach your goals.

That's nonsense.

(2 / 9)
There's absolutely nothing wrong with HTML and CSS.

But they aren't necessarily the foundation that you need when starting out.

Yes, they are simple to learn compared to a fully-fledged programming language, but they are also very different.

(3 / 9)
If you are looking forward to being a web developer, by all means, start with HTML, CSS, and JavaScript.

(And you can stop reading this thread now.)

If you aren't focused on the web and want to become a more general developer, HTML and CSS will not get you closer.

(4 / 9)
Recommending HTML and CSS to someone starting out is like recommending them to learn Word.

Both are useful. Neither will make what's coming simpler.

(5 / 9)
Instead, I'd recommend people to start with a programming language: Python, JavaScript, Java, C#, etc.

These languages have something in common that HTML and CSS don't: You'll learn the necessary programming fundamentals that will later translate between languages.

(6 / 9)
Here are some of the things that you need:

* Conditionals
* Loops
* String manipulation
* Functions
* Variables
* Lists, Maps, and other data structures
* Recursion
* OOP

You'll get most of these across programming languages —almost none of them in HTML and CSS.

(7 / 9)
I'll repeat it just in case I wasn't clear before:

HTML and CSS are great, useful, and you need them to be a web developer.

But they aren't a requirement —or even a proper introduction— to general programming. You could even argue they are a detour.

(8 / 9)
If you want to get started right away and don't know what you want to do, start with HTML and CSS: the learning curve is manageable, and you'll see results fast.

If you have other ambitions, pick up a programming language and start there.

(9 / 9)

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More from @svpino

3 Dec
Transitioning from Software Engineering to Machine Learning.

🧵👇
I'll tell you my story.

It might work for you. It might not.

Hopefully, it gives you another perspective. Hopefully, it helps.

(2 / 14)
Many people see "Software Engineering" and "Machine Learning Engineering" as two completely different specialization areas.

There are many differences, for sure.

But I personally like to think about them as a single, fluid, all-encompassing position.

(3 / 14)
Read 14 tweets
1 Dec
Django versus Flask versus FastAPI.

🐍 🧵👇
Django

▫️ Rapid development
▫️ A lot of out-of-the-box functionality
▫️ Easy for building complex, full web applications
▫️ MVC design paradigm
▫️ Robust security features
▫️ Extensible (a lot of components out there)
▫️ Large community

👇
Flask

▫️ Very light
▫️ Doesn’t make decisions for you
▫️ Doesn’t bring anything that you don’t need
▫️ Modular, so it’s easy to extend
▫️ You can plug in your favorite ORM
▫️ Great documentation
▫️ Very easy to start with
▫️ Large community

👇
Read 5 tweets
30 Nov
Here is every course that I've taken over the last 5 years to work full-time in Machine Learning applications:

🧵👇
(I took the following four classes while going through my Masters at Georgia Tech):

- Machine Learning
- Reinforcement Learning
- Reinforcement Learning for Trading
- Computer Vision

👇
(The following three courses are available through Coursera, and I recommend them for anyone trying to start):

- Machine Learning
- Deep Learning Specialization
- TensorFlow In Practice Specialization

👇
Read 5 tweets
25 Nov
Working on problems is the best way to learn Machine Learning.

Here are 10 projects to start your journey.

🧵👇
I picked all 10 projects from Kaggle.

When you are getting started, having a community ready to help is very important.

Also, every one of these problems has been solved by many people, and you can find those answers if you get stuck!

👇
I sorted the problems in the way I'd recommend you to start.

They more or less increase in complexity as you move through the list.

Let's get started!

👇
Read 14 tweets
24 Nov
Machine Learning doesn't need to be overwhelming.

Here is a strategy that you can use to get started without too many distractions.

🧵👇
If you start today, you'll probably feel overwhelmed by how much —apparently— you need to understand.

But it doesn't need to be like that.

You can take a much more practical approach to learn what you need and start providing value right away.

👇
Instead of starting "from the beginning," you can hack your way "from within."

The idea is simple:

1. Pick a simple problem —or an area— that's interesting to you.

2. Take the necessary steps to learn how to solve that problem.

3. Keep adding complexity as you see fit.

👇
Read 11 tweets
22 Nov
10 questions that spark conversations, make you think, and give you a solid foundation of practical Machine Learning.

🧵👇
(Some) interviews are broken.

They focus on trivia and expect candidates to recall concepts that aren't even relevant for the job.

This is garbage.

Instead, focus on problems that scientists and engineers face every day while doing their jobs: 👇
Acme Inc. is building a model to classify images in several different categories.

Unfortunately, they don't have a lot of images for some of the classes.

How would you handle such an imbalanced dataset?

(1 of 10)
Read 13 tweets

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