Santiago Profile picture
30 Nov, 5 tweets, 1 min read
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

👇
(These two are available on YouTube and free for anyone to watch):

- Reinforcement Learning (David Silverman)
- Stanford Computer Vision Course (Fei-Fei Li)

👇
There are also books, videos, papers, articles, and podcasts that have contributed to my education, of course, but the core of it is here.

What about you?

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More from @svpino

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
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
21 Nov
A plan to get a job as a Machine Learning Engineer.

🧵👇
Put in the work, level up, and get ready to demonstrate that you can deliver value.

You'll have to answer technical questions. Study up.

(If you aren't prepared, you won't pass the first round of interviews.)

(1 of 10)
Focus on showing, not telling.

What can you do today that will serve you as an asset when justifying your experience?

Creating a strong portfolio showing what you are capable of is the most important step you can take.

(2 of 10)
Read 12 tweets
19 Nov
Everything I know about great Software Developers.

🧵👇
1. Great Software Developers are humble.

They never put themselves above anyone else. They are willing to leverage existing solutions and listen to others.

(1 of 15)
2. Great Software Developers are self-motivated to learn.

They never stop improving and never get complacent. They understand the importance of growing their skills.

(2 of 15)
Read 24 tweets

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