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Santiago Profile picture
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6 May, 4 tweets, 5 min read
12 machine learning YouTube videos.

On libraries, algorithms, tools, and theory.

↓
1. Jupyter Notebooks:

2. Pandas:

3. Matplotlib:

4. Seaborn:
5. Numpy:

6. Decision Trees:

7. Neural Networks:

8. Scikit-Learn:
9. TensorFlow:

10. PyTorch:

11. Essense of Linear Algebra: youtube.com/playlist?list=…

12. Essense of Calculus: youtube.com/playlist?list=…

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More from @svpino

Santiago Profile picture
5 May
Machine learning education is broken.

If you are preparing for a research position, you are good. If you are looking to get out there and start solving problems, not even close.

Here are some thoughts so you can get ahead.

↓
Most classes, courses, and books cover the same road.

They start with a dataset. They finish with a working model. The focus is always on everything that happens in between.

Dataset → Model.

This is great, but not enough.
Real-life situations rarely start with a dataset, and they never end after you finish building your model.

Applying machine learning successfully is hard.

Here are a few examples that you should keep in mind.
Read 13 tweets
Santiago Profile picture
1 May
A little over 12 years ago, the police started building a case against me.

That was stressful. They were watching. They wanted to take me off the streets.

Here is the story of how I fled Cuba and came to the United States.

↓
After finishing college, I started taking freelance projects.

That was illegal. The Cuban government didn't allow people to make money working for foreign companies.

If you were lucky, you could get 2 years in jail. They called it "illicit enrichment."
We were a small group of friends. We met at my house every morning.

We paid a foreign national for Internet access. Cubans weren't allowed to buy it, so we had to get creative.

It was a 56kbs connection shared across 4 computers.
Read 13 tweets
Santiago Profile picture
30 Apr
We've all heard the horror stories.

Are you ready for machine learning math? Are you sure you can download a library, go through a course and make things happen?

This is some unsolicited advice.

↓ 1/10
Back then, we had to write custom training loops. Every time.

We all heard horror stories about the complexity of statistics and how ugly linear algebra was. This was a real thing.

The barrier to start with machine learning was high and full of thorns.

↓ 2/10
Today, things are different.

The lack of programming skills is a much bigger hurdle than not understanding how derivatives work.

Wanna have a better chance? Learn to code today. Worry about math tomorrow.

↓ 3/10
Read 10 tweets
Santiago Profile picture
28 Apr
Free machine learning education.

Many top universities are making their Machine Learning and Deep Learning programs publicly available. All of this information is now online and free for everyone!

Here are 6 of these programs. Pick one and get started!

↓
Introduction to Deep Learning
MIT Course 6.S191
Alexander Amini and Ava Soleimany

Introductory course on deep learning methods and practical experience using TensorFlow. Covers applications to computer vision, natural language processing, and more.

introtodeeplearning.com
Deep Learning
NYU DS-GA 1008
Yann LeCun and Alfredo Canziani

This course covers the latest techniques in deep learning and representation learning with applications to computer vision, natural language understanding, and speech recognition.

atcold.github.io/pytorch-Deep-L…
Read 8 tweets
Santiago Profile picture
27 Apr
$5 only for the next 50 orders.

1,131 people have bought it. 99% 5-star ratings.

Don't like it, and you pay nothing.

Link here → gum.co/kBjbC/five
If you bought it already or aren't interested, like/retweet the original tweet, and you'll be supporting my work as much as if you were paying.

Thank you from the bottom of my heart!
40 left.
Read 5 tweets
Santiago Profile picture
27 Apr
Data is the core of machine learning.

It should not surprise you that most of the work you'll have to do is related to capturing, managing, processing, and validating data.

A few recommendations for those who would like to start.

↓ 1/7
As you get your feet wet, these are roughly some of the areas that you should cover:

• Data collection
• Data visualization
• Imputation
• Handling outliers
• Encoding
• Normalization and scaling
• Binning and grouping

↓ 2/7
Here is a good, introductory, free course provided by Google:

"Data Preparation and Feature Engineering in ML." — developers.google.com/machine-learni…

It covers the process of collecting, transforming, splitting, and creating datasets that machine learning algorithms can use.

↓ 3/7
Read 7 tweets

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