Santiago Profile picture
9 Sep, 5 tweets, 2 min read
It took me 4 years to complete my Master's while I was working full time (2015 - 2019).

It's a Master of Science in Computer Science with a Machine Learning Specialization.

Here are all courses I had to complete to finish the program: 🧵👇
For the Machine Learning Specialization, I needed 15 hours that I distributed across the following courses:

1. Machine Learning
2. Computer Vision
3. Reinforcement Learning
4. Intro to Graduate Algorithms
5. Machine Learning for Trading

👇
To complete the program, I needed another 15 hours (but I finished 18):

6. Database Systems Concepts and Design
7. Software Development Process
8. Software Architecture and Design
9. Human-Computer Interaction
10. Advanced Operating Systems
11. Software Analysis and Testing

👇
11 courses over 4 years (approximately 1 course per semester.)

I studied at @GeorgiaTech on their amazing @GTOMSCS.

I cannot describe how much I learned and how much my life changed because of this program.

👇
You don't need a degree to succeed in Tech. But don't believe that going to school is not incredibly beneficial!

The amount of effort and dedication you need to finish school says a lot about your commitment to learning.

(And the skills you'll get will stay with you forever.)

• • •

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More from @svpino

10 Sep
Here are 20 fundamental questions that you need to ace before getting a Machine Learning job.

Almost every company will ask these to weed out non-prepared candidates. You don't want to show up unless you are comfortable having a discussion about all of these.

🧵👇
Of course, this is not an exhaustive list. There are many more topics and concepts you should master before applying for a job.

But hopefully, these will give you an idea of where you stand today.

🏃‍♂️Let's get started!

👇
▫️ Warming up ▫️

1. Explain the difference between Supervised and Unsupervised methods.

2. What's your favorite algorithm? Can you explain how it works?

3. Given a specific dataset, how do you decide which is the best algorithm to use?

👇
Read 9 tweets
8 Sep
Artificial Intelligence can be a bitch.

Here are 6 high-profile projects that have miserably failed and have made the respective companies look really foolish:

🧵👇
1⃣ Back in 2015, a software engineer reported that Google Photos was classifying his black friends as gorillas.

The algorithm powering the service was unable to properly classify some people of color 🤦!

Here is the story: theverge.com/2018/1/12/1688…

👇
2⃣ Back in 2016, Amazon had to scrap it's AI recruiting tool because it discovered that the system taught itself that male 👨 candidates were preferable, and penalized every resume that pointed to a female 👩 candidate.

Here is the story: reuters.com/article/us-ama…

👇
Read 10 tweets
7 Sep
Got asked a ton of questions about Machine Learning!

I decided to build a short FAQ to help you move forward.

Here are my answers to the 10 most frequently asked questions about getting into Machine Learning: 🧵👇
1⃣ Do I need Probabilities / Statistics / Linear Algebra to get started?

All of these help tremendously, especially if you want to understand how the algorithms work.

But they aren't a hard requirement to start applying some of the algorithms.

👇
2⃣ How relevant is a Ph.D. or MS degree to get a job?

Companies are currently asking for degrees to weed out people that apply to jobs prematurely.

But degrees aren't a requirement most of the time. Your skillset is the most important factor.

👇
Read 12 tweets
5 Sep
One of the best steps I’ve taken as a Software Engineer has been to get into Machine Learning.

If you are looking for what's next in your career, here are some pointers to get you started: 🧵👇
I always answer “what would you recommend next?” with "Machine Learning."

Here is why:

1⃣ Not only we are barely touching the surface of how Machine Learning will transform our lives in the next 10 years, but the need for qualified professionals will continue to rise.

👇
2⃣ As of today, Machine Learning is one of the fields that pay the most money, at least in the United States.

3⃣ There's huge demand, but there aren't many people competing in the market which opens many opportunities.

👇
Read 18 tweets
31 Aug
I've been in leadership positions for over a decade, and during that time, I've helped sell millions of dollars in software services.

Here are some of the things that I've learned about charging money for your skills. 🧵👇
First, we need to talk about hourly pricing.

Everyone will tell you to get away from hourly pricing and more into value-based pricing.

I agree.

But here is the thing: value-based pricing doesn't work a lot of the time. Hourly pricing always works.

👇
A lot of clients don't fall for the "I'm increasing your sales by $50,000 a year, so my website is worth $20,000" strategy.

That $50,000 is —most of the time— a bad guesstimate. You can't pretend to understand a business that well in a few days. And clients will call BS.

👇
Read 16 tweets
30 Aug
This is a thread about good software design.

— Because we need more of it, not less —

🧵👇
A fundamental characteristic of good software is its ability to change.

You can also call this "flexibility."

You want your software to be flexible, to bend under pressure.

This means that you can handle —with relative simplicity— any changes that come your way.

👇
Software is not static:

▫️Ideas evolve
▫️Complexity increases
▫️Users become more sophisticated
▫️Competitors show up
▫️Libraries get deprecated
▫️New technology becomes available
▫️Team members go. Others come

Change is the only constant you'll have to face at every step.

👇
Read 6 tweets

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