Santiago Profile picture
7 Apr, 12 tweets, 3 min read
16 key takeaways about starting a career as a machine learning engineer.

↓ 1/10
1. There are more courses out there than you'll ever need. Pick one and finish it. There's no such thing as "the best course."

2. You probably already know most of the math you'll need to get started.

↓ 2/10
3. Don't be discourage by introductory courses that claim you need a ton of math as a prerequisite. Most don't.

4. You want to be proficient with Python. Other languages are great and all, but Python rules.

↓ 3/10
5. The better developer you are, the easier machine learning will be for you. If you have a hard time programming, it will cost you to make progress.

6. Understanding computer science fundamentals is usually overlooked but extremely beneficial.

↓ 4/10
7. While learning, don't worry about the code and invest your time understanding how and why things work.

8. Some algorithms are complex as shit. 99 out of 100 times, you can just use them without worrying about how they work.

↓ 5/10
9. "Understand every detail of an algorithm if you want to use it" is bullshit advice.

10. Machine learning is not different than any other subject you may learn. You can start at a very high level and get deeper into details as you need them.

↓ 6/10
11. There are 375,475,484 algorithms out there. In practice, you'll probably use a handful of them.

12. You don't need a GPU to get started. You don't need a GPU most of the time. You will at some point for deep learning, and you can solve it online for free.

↓ 7/10
13. Most machine learning educative learning materials end when you finish training your model. In real life, this is where the fun starts.

14. The best solution for a problem is not necessarily machine learning-related. In fact, this is the case most of the time.

↓ 8/10
15. The loudest machine learning voices right now come from academia. Make sure you understand that a research position is very different than working in the industry.

16. Pragmatism is a great trait when building a career centered on providing value.

↓ 9/10
If you are looking for a pragmatic perspective on building machine learning systems, follow me @svpino and subscribe to my newsletter

It's free, and you get one story every Friday right in your inbox.

Absolutely! A lot of these things are the same across different disciplines.

If you tune out all of the noise, you can focus on delivering value.

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More from @svpino

9 Apr
About getting a job with machine learning, things you should do during an interview, and some random thoughts that may help you land a job

Before getting a job or making any money, you need to show that you are competent.

Put in the work right now and level up.

There are no hacks, no shortcuts, or magic words that will convince anyone to pay you for something you don't know.

You don't need to get a Ph.D. or a Master's to work as a machine learning practitioner.

I understand many companies are still asking for a degree, but there are many that aren't.

Find and apply to those.

Read 20 tweets
7 Apr
Today is #GumroadDay, so let's celebrate with style!

$5 for "How to get started with machine learning" TODAY ONLY!

You can kick off your machine learning career for less than the price of a Starbucks, but this one you can return if you don't like it!
This is working. #GumroadDay is nuts!
Thanks to everyone that has supported me with this course!

Money to feed the family is what affords me the time to post content and focus on helping people with machine learning.

Even if you aren't buying this, like/retweet for visibility!

Read 10 tweets
6 Apr
There are a lot of moving pieces on a machine learning system.

This is a thread covering the backbone of the process, from data engineering all the way to a retraining pipeline.

Let's start. ↓
Everything starts with a problem you want to solve.

For example, you want to predict your company's sales in the next 12 months, and you have the last two years' worth of sales in a database.

When use case and data align, you are good to go!

The first step is to prepare the data to train a machine learning model that predicts future sales.

You have the data already, but you may need to transform it into a format that's easier for the model.

This process is called "Data Engineering."

Read 28 tweets
5 Apr
Many people who want to start with machine learning face a big hurdle:

They think they can't do it at their current company.

But more often than not, this is not the case. This is a thread about things you can do to get past this.

↓ 1/12
First, don't worry if your company doesn't have a machine learning engineer position yet.

Look at this as a good opportunity!

Nobody has any expectations about the job yet, so you'll get to set the pace.

↓ 2/12
Focus on doing the work. The actual position, title, compensation, and other details will follow later.

Here is where you need to get creative, and these are two different strategies that I've seen working.

↓ 3/12
Read 12 tweets
4 Apr
Learning a new language is not an obvious decision, especially when you are just starting in the industry.

Here are 10 frequently asked questions about learning Python 🐍. Hopefully, these give you the answers you are looking for.

1. Can I learn Python for free?

Yes. There are multiple YouTube videos, tutorials, and courses that will teach you Python for free.

But if you can afford it, I'd recommend you find a good MOOC that gives you some structure.

↓ 1/10
2. Is Python hard to learn?

It's not, especially compared with other languages out there.

That being said, becoming an expert is a life-long journey.

But one year of experience is more than enough for you to do whatever you decide to do.

↓ 2/10
Read 14 tweets
3 Apr
The Python 🐍 community on Twitter is amazing!

If you are a Python developer or you are looking to get started, introduce yourself below and let others connect with you👇
Hi 👋, I'm a machine learning engineer, and I've been coding exclusively with Python for 7 straight years.

I believe that Python is one of the most versatile languages you can learn today, and it's an investment with the potential to change your life.
The best part about this are the connections that this enables.

People saying hi, making study groups, asking questions, and helping each other.

Make sure to look through the comments. A lot of likeminded people willing to partner with you and do this together!
Read 4 tweets

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