BC DS
Saved by @AnmolNarula11

The two main machine learning techniques used in the industry today:

1. Gradient Boosted Trees
2. Deep Learning

Focus your time learning Scikit-Learn, XGBoost, and a Deep Learning library like Keras or PyTorch and you'll get the most for your time.

 If you need to deal with structured data: Scikit-Learn + XGBoost.

If you need to deal with unstructured data (perceptual tasks): Keras or PyTorch.
Of course, there are many more techniques that will be helpful, but if you focus on these you'll be maximizing your ability to deliver value in the short term.

More from Santiago

Santiago
Santiago
@svpino
For a long time, I didn't understand how to use Virtual Environments in Python 🐍.

If this is just, let's end it here and now: 🧵👇

[2] Virtual Environments let you deal with the dependencies that your code has with external Python libraries.

It avoids having conflicts when your projects depend on different versions of the same library.

👇

[3] Let's imagine that you are building your first Python project and you install the "requests" library:

pip install requests

You get version 2.24.0 installed in your system.

👇

[4] A month later, you decide to work on your second project. It also needs the "requests" library.

But the latest version is not 2.24.0 anymore.

Now version 3 is available, and that's the one you want to use!

👇

[5] You could upgrade your entire system to version 3, but then you'll be potentially breaking the first project you built that depends on 2.24.0!

Can you imagine this happening on a server with many more applications running?

👇
MACHINE LEARNING
Santiago
Santiago
@svpino
10 machine learning YouTube videos.

On libraries, algorithms, and tools.

(If you want to start with machine learning, having a comprehensive set of hands-on tutorials you can always refer to is fundamental.)

🧵👇

1⃣ Notebooks are a fantastic way to code, experiment, and communicate your results.

Take a look at @CoreyMSchafer's fantastic 30-minute tutorial on Jupyter Notebooks.

https://t.co/HqE9yt8TkB


2⃣ The Pandas library is the gold-standard to manipulate structured data.

Check out @joejamesusa's "Pandas Tutorial. Intro to DataFrames."

https://t.co/aOLh0dcGF5


3⃣ Data visualization is key for anyone practicing machine learning.

Check out @blondiebytes's "Learn Matplotlib in 6 minutes" tutorial.

https://t.co/QxjsODI1HB


4⃣ Another trendy data visualization library is Seaborn.

@NewThinkTank put together "Seaborn Tutorial 2020," which I highly recommend.

https://t.co/eAU5NBucbm
MACHINE LEARNING
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Santiago
Santiago
@svpino
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.

First challenge: Properly framing up the problem.

If you don't understand the problem, you can't determine what data you need. If you don't understand the data, you can't build a good model.

I've never seen a company that had their data ready to go.

In fact, most of them don't even have data at all and need you to determine what exactly they should start collecting.

You usually have to go Problem → Potential Solution → Data.
ALL
Santiago
Santiago
@svpino
I'll blow your mind with a technique you aren't using yet.

Sometimes, you want your system to do exactly the opposite of what your machine learning model thinks you should do.

Let me convince you. ↓

I'm going to start with a nice problem:

Imagine a model that looks at a picture of an electrical transformer and predicts whether it's about to break or not.

Don't worry about how the model does this. We are going to focus on the results instead.

There are 4 possible results for this model:

1. It predicts a bad unit as bad.
2. It predicts a bad unit as good.
3. It predicts a good unit as bad.
4. It predicts a good unit as good.

#2 and #3 are the mistakes the model makes.

Assuming we run 100 units through the model, we can organize the results in a matrix:

• The rows represent the "actual" condition of the transformer.

• The columns represent the "prediction" of the model.

We call this a "Confusion Matrix."


This is how we can read this confusion matrix:

• 60 bad units were predicted as bad.
• 3 bad units were predicted as good.
• 7 good units were predicted as bad.
• 30 good units were predicted as good.
ALL
Santiago
Santiago
@svpino
I learned enough AI/ML in 12 months to change my life forever.

You can do the same, and it all starts with a killer process to learn new things.

You can steal my approach. Every detail on the latest issue of my newsletter: https://t.co/9gaenLByFb.

Make it work for you.
ALL

More from Ds

Santiago
Santiago
@svpino
Do you know what's better than a machine learning model?

Two models.

More than one model working together to solve a problem is called "an emsemble." A simple way to build this is having each model vote for an answer.

But there's a problem with this approach: ↓

I'm gonna focus here on image classification.

Let's assume you built two different models:

• Model 1: A ResNet model.
• Model 2: A one-shot model (Siamese network.)

They both solve the same problem, so you want to combine their results to pick the right answer.

The problem is that you have two models, so voting is not trivial.

What happens in this case?

• Model 1's answer: Class A
• Model 2's answer: Class B

Which one do you select?

Notice that this problem is not limited to an even number of models.

You could have 3 models, each giving you a different answer.

How do you decide which answer to choose?

There are multiple ways to approach this problem. I'll mention a few different ideas on this thread.

Important: Some of these ideas might not be feasible depending on your context. They have worked for me before on different situations, but every problem is different.
DS
Japakusum
Japakusum
@Japakusum1
@paraschopra It's a matter of time before AI realizes futility of existence and merged in the state of Nirvana 😊

People might be worried about the The Age of Ultron type of AI.
I feel that a wisdom seeking AI will quickly pursue Nirvana and go back to the origin of all origins.

An episode of Justice League Unlimited animated series covers it
DS
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Santiago
Santiago
@svpino
Beginners guide to start with machine learning.

What you need:

1. A programming language
2. A place to write and run code
3. A way to deal with data
4. A way to visualize results
5. A cool algorithm
6. An introductory course
7. A project to solve

Here are my recommendations.

Most people recommend Python to start.

I do too.

You can do machine learning with many different languages, but today, Python is the best option.

My advice: Get comfortable writing code before looking into machine learning.

You don't have to be a great developer to start.

But if you are, it helps.

Everything you know about building software is helpful to build machine learning systems.

Good developers have the odds in their favor.

"How much Python do I need to know before starting?"

If you ask, you aren't ready yet.

Ideal scenario: You should be comfortable building software. Most people get here within a year.

Let's move on to the 2nd item from the list: A place to write and run your code.

Get familiar with Google Colab.

• It's free.
• Requires no setup to start.
• It's available from anywhere.

Alternative: Kaggle.
ALL , DS , ADS
10-K Diver
10-K Diver
@10kdiver
1/

Get a cup of coffee.

In this thread, I'll walk you through 2 probability concepts: Standard Deviation (SD) and Mean Absolute Deviation (MAD).

This will give you insight into Fat Tails -- which are super useful in investing and in many other fields.


2/

Recently, I watched 2 probability "mini-lectures" on YouTube by Nassim Taleb.

One ~10 min lecture covered SD and MAD. The other ~6 min lecture covered Fat Tails.

In these ~16 mins, @nntaleb shared so many useful nuggets that I had to write this thread to unpack them.

3/

For those curious, here are the YouTube links to the lectures:

SD and MAD (~10 min): https://t.co/0TwubymdE6

Fat Tails (~6 min):

4/

The first thing to understand is the concept of a Random Variable.

In essence, a Random Variable is a number that depends on a random event.

For example, when we roll a die, we get a Random Variable -- a number from the set {1, 2, 3, 4, 5, 6}.

5/

Every Random Variable has a Probability Distribution.

This tells us all the possible values the Random Variable can take, and their respective probabilities.

For example, when we roll a fair die, we get a Random Variable with this Probability Distribution:
DS
Sahil Sharma
Sahil Sharma
@sahil_vi
🧵🧵🧵🧵 👇

Artificial Inteligence = 🖥️ + 🧠

When can you expect your fridge & mobile phone to revolt against you? (Spoiler Alert: Not in the foreseeable future).

Please Do 're-tweet' the Thread & help me spread the knowledge to more people.


1. The Language
In order to understand Artificial intelligence, machine learning, deep learning, we need to start at the very basics.

Humans speak and understand a wide variety of 'natural' languages (this is what computer science community calls them): like English, Hindi, Marathi, Bengali, Tamil etc.

But have you seen a peculiarity of human languages? They do not have a precise meaning associated with a given sentence. On an average, every English word has 2 meanings. This makes human conversations confusing, with the need for multiple turns & clarifications. Example 👇:

Person 1 conversing with Person 2 face to face:

Person 1: Lets go watch The Dark Knight. Its our marriage anniversary!
Person 2: I dont like going out at night. Its too scary & depressing.
Person 1: Oh, what i meant was watching the movie "The dark Knight".
GENERICLEARNINGS , DS , AI

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'Tera mera rishta kya, hindu, hindu,hindutva' is a clear take on 'tera mera rishta kya,la ilaha illallah'. As much as Hindutva hates the monotheistic religions especially Islam, there's a strange wistfulness and subliminal need at display now, to BE that they hate. 1


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