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Santiago

@svpino

7 Apr

16 key takeaways about starting a career as a machine learning engineer.

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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.

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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

Read 12 tweets

Santiago

@svpino

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

Santiago

@svpino

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.

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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.

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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.

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Read 12 tweets

Santiago

@svpino

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.

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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.

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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

Santiago

@svpino

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

Santiago

@svpino

3 Apr

25 True|False machine learning questions that are horrible for interviews but pretty fun to answer.

Most importantly: they will make you think and will keep your knowledge sharp.

These are mostly beginner-friendly.

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1. A "categorical feature" is a feature that can only take a limited number of possible values.

2. Precision is a performance metric that defines a classification model's ability to identify only relevant samples.

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3. Recall is a performance metric that defines a classification model's ability to identify all relevant samples.

4. One-hot encoding is an excellent solution to transform categorical features with high cardinality.

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Read 14 tweets

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