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Santiago Profile picture
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15 Jun, 11 tweets, 1 min read
If you are planning to get started with Machine Learning:

My introductory course is on sale:

• 50 copies @ $5, starting right now.
• Free copies for those who can't afford this.

gum.co/kBjbC/five

If you want to support my content, like/retweet this. Everyone wins.
10 copies sold, 40 copies left @ $5.
20 copies sold, 30 copies left @ $5.
20 copies left @ $5.
50 new copies @ $6 starting now.
35 copies left @ $6. 15 copies gone.
25 copies left @ $6.
Only 15 copies left @ $6.
Down to the final 5 copies at this price.
All 100 copies are now sold.

Starting now, and for the next 12 hours, price is $7. Still 50% off.
Final 3 hours.

• • •

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More from @svpino

Santiago Profile picture
15 Jun
Do you know what's holding you back?

The same boring projects than everyone else is working on. How do you break off the mold and make a difference?

Thread: 7 machine learning projects that will teach you the technical skills you need to succeed out there.
Today, companies are dumping insane amounts of money on people with the right skills.

But here we are, showing up with the same "MNIST Digit Recognition" and "Iris dataset" experience.

These are good to start, but you need to take your learning to another level.
The good news for you: Most people can't be bothered.

Most people will bookmark this thread and will never do anything with it.

Even if you do one of these 7 projects, you'll be doing more than 99% of everyone who reads this!

It has never been easier to stand out!
Read 19 tweets
Santiago Profile picture
14 Jun
How close are we to building a truly intelligent agent?

Most scientists think we are still decades away, but today, a group of scientists from @DeepMind claims they know how to get there.

Let's talk about what's going on.
What is "Artificial General Intelligence" (AGI)?

An agent capable of learning any intellectual task than a person can also learn.

Today, AI has been limited to systems that can learn particular tasks. A system that can learn anything you teach it, just like a human, is AGI.
Unfortunately, there's no way to build such a general, intelligent agent without formulating a custom solution for every individual task.

This sucks. This doesn't scale. This doesn't get us to AGI.

But maybe we aren't that far off...
Read 16 tweets
Santiago Profile picture
12 Jun
6 lies you have been told about machine learning:

1. You need a lot of math to start
2. You need a Ph.D. to get a job
3. You always need a lot of data
4. You need to buy expensive hardware
5. It's hard to become proficient in it
6. It's the solution for most problems

Bullshit.
In the last 6 months, I've posted more than 100 threads here on Twitter talking about machine learning and how you can build a career on it.

And I'm just getting started!

Stay tuned. A lot more is coming.
First misconception: All machine learning is hardware-hungry.

Deep learning stretches you, but outside that, it gets much better.

If you need GPUs/TPUs, there are many free/cheap options you can use, especially while learning.

Read 9 tweets
Santiago Profile picture
11 Jun
Software developers want to get into machine learning.

Many make the same mistakes. I've seen a few, and I have some ideas on how to avoid them.

This is what I've learned ↓
Lesson 1

Most people love the idea of starting something new. Only a few take the first step.

Preparing for something new is fun and exciting. It can also turn into glorified procrastination.

Stop collecting resources. Take what's right in front of you and run with it.
Lesson 2

Learning is a marathon, not a sprint. Strap yourself for a long, lifelong road.

If you are looking to make a quick buck, look elsewhere. If you are looking for shortcuts, this ain't it.

Make sure you come for the right reasons and are willing to go the distance.
Read 12 tweets
Santiago Profile picture
10 Jun
Machine learning superpower: Be the one that makes better predictions.

I can teach you how to do this by putting 2+ models together.

Thread: beginner-friendly introduction to Ensembles.

• What are they?
• Why do they work?
• Real-life examples.
• Practical tips.

↓
A group of models working together is called an "ensemble."

Instead of using a single model, you could build 2 different models and have them vote to select the best answer.

You could also build 3, 4, or however many models you want.

This is powerful.
Here is the surprising part:

Ensembles usually perform better than all of their individual models.

Let's look at one example.
Read 22 tweets
Santiago Profile picture
9 Jun
There are thousands of machine learning algorithms out there, but that's mostly noise.

You'll rarely need more than a handful.

A good start:

• Linear/Logistic Regression
• Decision Trees
• Neural Networks
• XGBoost
• Naive Bayes
• PCA
• KNN
• SVM
• t-SNE
Why these 9 instead of your favorite ones?

No specific reason. Your list will certainly include algorithms that I haven't even heard about.

But you must start somewhere, and these are certainly a good foundation.
In case you are curious, I also studied the following algorithms in the first couple of years of starting with machine learning:

• Random Forest
• AdaBoost
• K-Means
• Expectation Maximization
• Simulated Annealing
• Genetic Algorithms
Read 5 tweets

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