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
• 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
I had no choice. I had to study a lot of algorithms in a short time.

I don't recommend this approach.

I ended up with a wide but shallow knowledge of the field. It took a lot of work to make that knowledge usable.

I'd rather know 2-3 techniques but know them well.

If you are starting with machine learning:

• Focus on a few foundational algorithms.
• Try to become proficient at using them.
• Refine your list as you gain experience.

Do you feel these threads help? Then you don't want to miss what's coming. Stay tune @svpino.,

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More from @svpino

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 16 tweets
8 Jun
Last week, an Italian artist sold an invisible sculpture for $18,300.

That's a lot of money for a lot of nothing!

I have a better idea for your money:

I can help you start your machine learning career. Something that will pay *you* for the rest of your life.

For the next 24 hours, you can grab my course for a massive 60% discount!

$6 only. That's the price of a cup of coffee where I live!

(Almost 2,000 customers with 208 reviews.)

If you still can't afford this, let me know, and I'll send you a free copy.
14 more hours, and the price goes back to $15.
Read 6 tweets
8 Jun
The 4 stages of a machine learning project lifecycle:

1. Project scoping
2. Data definition and preparation
3. Model training and error analysis
4. Deployment, monitoring, and maintenance

Here are 29 questions that you can use at each step of the process.

Project scoping

• What problem are we trying to solve?
• Why do we need to solve this problem?
• What are the constraints?
• What are the risks?
• What's the best approach to solving it?
• How do we measure progress?
• What does success look like?
Data definition and preparation

• What data do we need?
• How are we going to get it?
• How frequently does it change?
• Do we trust the source?
• How is this data biased?
• Can we improve it somehow?
• How are we going to clean it?
• How are we going to augment it?
Read 10 tweets
7 Jun
Here is a photo from the newspaper of a communist island.

I'm the one standing. This was 20 years ago.

I've been developing software for 25+ years, and I've learned a few things.

I didn't have Internet back then, but now that I do, I can share 3 lessons with you:

Look at that photo again.

This was early 2000.

Those were the best computers Cuba had to offer to our Computer Science faculty. Outdated but good enough.

In a country where owning a personal computer was a crime, it was all we had.
One thing was missing: There was no Internet.

I know this might be hard to understand, so I'll rephrase:

We were going through our Computer Science bachelor's with no Internet access.

The entire wealth of information we had fit in a couple of books.
Read 14 tweets
6 Jun
Good systems produce outstanding results.

↓ Some of my recommendations:

• Improve as a developer
• Improve your communication
• Take a course. Take another. Repeat.
• Solve problems. Many of them.
• Teach others.
• Analysis first. Code is secondary.
• Stay curious.
“Tutorial hell” is only when you focus on consumption and neglect production.

Solve problems and put what you learn out there.
Curiosity pushes me to dig deeper. An infinite number of "but why?" questions.

There's something new and interesting on every layer you uncover.

And the more you dig, the better your understanding and the greater your capacity to create something new.

Read 4 tweets
4 Jun
Many online courses are useless. They will not get you anywhere.

But there are gems out there.

Here is a curated list that will help you build a machine learning career without paying a fortune in tuition fees. ↓
5 specializations and 1 course, all from a single platform: Coursera.

Take these in order, and you'll end up with more than enough ammunition to tackle real-life problems.

Here is your roadmap:…
If you find this useful, follow me @svpino, and I'll help put some practical machine learning thoughts right on your timeline.

And if you are looking for ideas that don't fit on Twitter, you can join the other 3,200+ subscribers of my newsletter:
Read 6 tweets

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