If you need to deal with unstructured data (perceptual tasks): Keras or PyTorch.
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 unstructured data (perceptual tasks): Keras or PyTorch.
More from Santiago
12 machine learning YouTube videos.
On libraries, algorithms, tools, and theory.
↓
1. Jupyter Notebooks: https://t.co/HqE9yt8TkB
2. Pandas: https://t.co/aOLh0dcGF5
3. Matplotlib: https://t.co/tKADpmihkh
4. Seaborn: https://t.co/s8EUxh6x1f
5. Numpy: https://t.co/pJoc0Lfjwm
6. Decision Trees: https://t.co/tKtUpO1K3l
7. Neural Networks: https://t.co/bc7emyjc9q
8. Scikit-Learn: https://t.co/LrKG7cMxRq
9. TensorFlow: https://t.co/fhO6T9sblU
10. PyTorch: https://t.co/5w9mJxijdd
11. Essense of Linear Algebra: https://t.co/o3kOnxl90i
12. Essense of Calculus: https://t.co/rfo7v0cpR4
On libraries, algorithms, tools, and theory.
↓
1. Jupyter Notebooks: https://t.co/HqE9yt8TkB
2. Pandas: https://t.co/aOLh0dcGF5
3. Matplotlib: https://t.co/tKADpmihkh
4. Seaborn: https://t.co/s8EUxh6x1f
![](https://pbs.twimg.com/media/E0skpffWQAEMSN_.png)
5. Numpy: https://t.co/pJoc0Lfjwm
6. Decision Trees: https://t.co/tKtUpO1K3l
7. Neural Networks: https://t.co/bc7emyjc9q
8. Scikit-Learn: https://t.co/LrKG7cMxRq
![](https://pbs.twimg.com/media/E0skpfrWQAUF9z0.jpg)
9. TensorFlow: https://t.co/fhO6T9sblU
10. PyTorch: https://t.co/5w9mJxijdd
11. Essense of Linear Algebra: https://t.co/o3kOnxl90i
12. Essense of Calculus: https://t.co/rfo7v0cpR4
![](https://pbs.twimg.com/media/E0skpfrXEAAameQ.jpg)
You gotta think about this one carefully!
Imagine you go to the doctor and get tested for a rare disease (only 1 in 10,000 people get it.)
The test is 99% effective in detecting both sick and healthy people.
Your test comes back positive.
Are you really sick? Explain below 👇
The most complete answer from every reply so far is from Dr. Lena. Thanks for taking the time and going through
You can get the answer using Bayes' theorem, but let's try to come up with it in a different —maybe more intuitive— way.
👇
Here is what we know:
- Out of 10,000 people, 1 is sick
- Out of 100 sick people, 99 test positive
- Out of 100 healthy people, 99 test negative
Assuming 1 million people take the test (including you):
- 100 of them are sick
- 999,900 of them are healthy
👇
Let's now test both groups, starting with the 100 people sick:
▫️ 99 of them will be diagnosed (correctly) as sick (99%)
▫️ 1 of them is going to be diagnosed (incorrectly) as healthy (1%)
👇
Imagine you go to the doctor and get tested for a rare disease (only 1 in 10,000 people get it.)
The test is 99% effective in detecting both sick and healthy people.
Your test comes back positive.
Are you really sick? Explain below 👇
The most complete answer from every reply so far is from Dr. Lena. Thanks for taking the time and going through
Really doesn\u2019t fit well in a tweet. pic.twitter.com/xN0pAyniFS
— Dr. Lena Sugar \U0001f3f3\ufe0f\u200d\U0001f308\U0001f1ea\U0001f1fa\U0001f1ef\U0001f1f5 (@_jvs) February 18, 2021
You can get the answer using Bayes' theorem, but let's try to come up with it in a different —maybe more intuitive— way.
👇
![](https://pbs.twimg.com/media/Eugq8PDWgAkxi2I.png)
Here is what we know:
- Out of 10,000 people, 1 is sick
- Out of 100 sick people, 99 test positive
- Out of 100 healthy people, 99 test negative
Assuming 1 million people take the test (including you):
- 100 of them are sick
- 999,900 of them are healthy
👇
Let's now test both groups, starting with the 100 people sick:
▫️ 99 of them will be diagnosed (correctly) as sick (99%)
▫️ 1 of them is going to be diagnosed (incorrectly) as healthy (1%)
👇
More from Ds
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:
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.
![](https://pbs.twimg.com/media/E1bJxQkUYAEvxiW.jpg)
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:
![](https://pbs.twimg.com/media/E1bWgXtVcBEYtW6.jpg)
You May Also Like
The YouTube algorithm that I helped build in 2011 still recommends the flat earth theory by the *hundreds of millions*. This investigation by @RawStory shows some of the real-life consequences of this badly designed AI.
This spring at SxSW, @SusanWojcicki promised "Wikipedia snippets" on debated videos. But they didn't put them on flat earth videos, and instead @YouTube is promoting merchandising such as "NASA lies - Never Trust a Snake". 2/
A few example of flat earth videos that were promoted by YouTube #today:
https://t.co/TumQiX2tlj 3/
https://t.co/uAORIJ5BYX 4/
https://t.co/yOGZ0pLfHG 5/
Flat Earth conference attendees explain how they have been brainwashed by YouTube and Infowarshttps://t.co/gqZwGXPOoc
— Raw Story (@RawStory) November 18, 2018
This spring at SxSW, @SusanWojcicki promised "Wikipedia snippets" on debated videos. But they didn't put them on flat earth videos, and instead @YouTube is promoting merchandising such as "NASA lies - Never Trust a Snake". 2/
![](https://pbs.twimg.com/media/DsX3HqMUcAAXe9P.jpg)
A few example of flat earth videos that were promoted by YouTube #today:
https://t.co/TumQiX2tlj 3/
https://t.co/uAORIJ5BYX 4/
https://t.co/yOGZ0pLfHG 5/