BC MACHINE LEARNING

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Santiago
@svpino 5 years, 6 months ago 1073 views

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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

5⃣ Numpy is another Python library that you will use every single day.

@keithgalli's "Complete Python NumPy Tutorial" is a great start.

https://t.co/Xg0YbuR8fz

6⃣ One of the most basic algorithms that you can learn is Decision Trees.

Watch @random_forests' video where he builds a decision tree from scratch:

https://t.co/tKtUpO1K3l

7⃣ It's hard to talk about machine learning without touching on neural networks.

Probably the best video out there that explains how neural networks work is @3blue1brown's:

https://t.co/OMJHiG7PIu

8⃣ Scikit-Learn is one of the most popular machine learning libraries out there.

@simplilearn's "Scikit-Learn Tutorial" is a great place to start.

https://t.co/efd1kmz07c

9⃣ TensorFlow is the most popular deep learning library that's currently used in the industry.

Here is a massive 7-hour tutorial of TensorFlow 2.0 produced by @freeCodeCamp.

https://t.co/BYUoAQJEeu

🔟 Finally, a great way to start getting familiar with machine learning is the bite-sized recipes published by Google.

This series is worth every minute.

Playlist: https://t.co/xDqhmNQoWg

If you are looking for real-life, hands-on information related to machine learning, follow me.

✌️

If you have questions or suggestions about topics you'd like to hear about, let me know.

More from Santiago

Santiago
@svpino

11 key concepts of Machine Learning.

— Supervised Learning Edition —

🧵👇

😜

Before starting, remember that, if you follow me, one of your enemies will be immediately destroyed (and you'll get to read more of these threads, of course.)

And if you don't follow me, well, you just hurt my feelings.

😜

1. Labels

(Also referred to as "y")

The label is the piece of information that we are predicting.

For example:

- the animal that's shown in a picture
- the price of a house
- whether a message is spam or not

👇

2. Features

(Also referred to as "x")

These are the input variables to our problem. We use these features to predict the "label."

For example:

- pixels of a picture
- number of bedrooms of a house
- square footage of a house

👇

3. Samples

(This is also known as "examples.")

A sample is a particular instance of data (features or "x.") It could be "labeled" or "unlabeled."

👇

Santiago
@svpino

One of the most popular activation functions used in deep learning models is ReLU.

I asked: "Is ReLU continuous and differentiable?"

Surprisingly, a lot of people were confused about this.

Let's break this down step by step: ↓

Let's start by defining ReLU:

f(x) = max(0, x)

In English: if x <= 0, the function will return 0. Otherwise, the function will return x.

If you draw this function, you'll get the attached chart.

Notice there are no discontinuities in the function.

This should be enough to answer half of the original question: the ReLU function is continuous.

Let's now think about the differentiable part.

A necessary condition for a function to be differentiable: it must be continuous.

ReLU is continuous. That's good, but not enough.

Its derivative should also exist for every individual point.

Here is where things get interesting.

We can compute the derivative of a function using the attached formula.

(I'm not going to explain where this is coming from; you can trust me on this one.)

We can use this formula to see whether ReLU is differentiable.

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Santiago
@svpino

A topic that comes up in every interview.

Bias, variance, and their relationship with machine learning algorithms. One of the most basic concepts that you have to know by heart.

Here is a simple summary that you will easily remember.

↓

Every machine learning algorithm deals with 3 types of errors:

1. Bias error
2. Variance error
3. Irreducible error

There's nothing we can do about #3.

Let's focus on the other two.

↓ 1/5

"Bias" refers to the assumptions the model makes to simplify the process of finding answers.

The more assumptions it makes, the more biased the model is.

"Variance" refers to how much the answers given by the model will change if we use different training data.

If the answers stay the same regardless of the data, the model has low variance.

Often, linear models are high-bias, and nonlinear models are low-bias.

Example low-bias algorithms:
• Decision Trees
• SVN
• kNN

Example high-bias algorithms:
• Linear Regression
• Logistic Regression

Santiago
@svpino

Interviews aren't only about technical skills.

Here are some questions to help you prepare.

🧵👇

Explain what you have been working on for the past few weeks.

What are the most exciting parts about that work?

What portion of it do you consider boring and why?

[2 / 10]

What specific libraries and frameworks are you familiar with?

What's the minimum set of libraries and frameworks that you'd recommend to any practitioner?

[3 / 10]

What kind of problems have you worked on in the past?

Can you list the specific use cases related to each one of these?

[4 / 10]

What is the most exciting project you have ever worked on?

What was your role and responsibilities on that project?

Why do you think your work on that project was important?

[5 / 10]

Santiago
@svpino

What's machine learning?

Not a framework. Not a different language. Not the latest "cool" thing around the Internet.

Thread: Let me convince you that machine learning is the best thing since sliced bread 🍞.

Let's start with a simple exercise:

"Write a function that returns 0 if a supplied number is even or 1 if the number is odd."

If you are a developer, I'm sure you know how to write this function. It should be simple, right?

To avoid using a specific syntax, let's represent this function as follows:

y = f(X)

Where:
• X → The input number
• y → The result (0 if even, 1 otherwise)
• f → The function that does the work

We can now get to the interesting part.

If you are a programmer, writing this function is simple.

You know the relationship between X (the input number) and y (the output), so you can easily write the rules (the function.)

In machine learning, we don't write the rules. We learn them.

Imagine we had an algorithm that processes many samples (X → y), learns the relationship between them, and comes up with the correct rules.

We call these samples "training data," and they would like like this:

• 2 → 0
• 5 → 1
• 8 → 0
• 4 → 0
• 3 → 1
• ...

More from Machine learning

Jan Giacomelli
@jangiacomelli

🤔 Python decorators

What are they? How do you use them?

🧵 Let's find out 👇

1️⃣ Decorator is a function or a class that wraps another function or class modifying its behavior.

So how does that work?

The first thing to know is that everything in Python is an object - functions too

2️⃣ That means they can be passed to another function as an argument or returned from a function

Functions that take other functions as an argument are called higher-order functions

3️⃣ In Python, you can define a function inside other function - such functions are called inner functions

4️⃣ To create a decorator you just need to apply all of that together

log_enter_leave is a decorator.

my_function is the function.

To alter my_function's behavior we reassign it applying log_enter_leave decorator.

Cassie Kozyrkov
@quaesita

1/🧵 Good #DataScience advice that breaks pretty much every rule you learned in class... a thread. (+full blog post linked)

English version: https://t.co/dG4l6vPFBT
Spanish version: https://t.co/gFAjPQ5clS

#AI #MachineLearning #Statistics #RStats

2/🧵 Allow your approach to be sloppy at first and burn some of your initial time, energy, and data on informing a good direction later. That's right, you're supposed to start sloppily ON PURPOSE.

3/🧵 Have a phase where the only result you’re after is *an idea of how to design your ultimate approach better.*

4/🧵 In other words, start with a pilot phase where the objective isn't finding answers, it's finding a good approach to finding answers.

5/🧵 That means you're encouraged (ENCOURAGED!) to start with everything your stats classes told you not to do:

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Pratham Prasoon
@PrasoonPratham

Are you planning to learning machine learning this year?

These are the Python frameworks you need to learn as an absolute beginner.

🧵👇

It can be overwhelming seeing all the machine learning frameworks at once and not even knowing what they do.

I made this thread so that your machine learning journey becomes just a little bit easier.

(2 / 11)

This thread will only focus on Python frameworks.

Why?
Because Python is the most common path into machine learning.

( If you have experience with any other language then learning Python will not be difficult at all. )

(3 / 11)

Here's the entire list:

- Pandas
- Numpy
- Matplotlib
- TensorFlow
- PyTorch
- Keras
- Scikit Learn

Let's see what they do.

(4 / 11)

You'll have to interact with csv files or databases on a regular basis to access data in machine learning, pandas helps you do just that.

The cool thing about Pandas is that it actually doesn't change the original data...

(5 / 11

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?

👇

Santiago
@svpino

11 key concepts of Machine Learning.

— Supervised Learning Edition —

🧵👇

😜

Before starting, remember that, if you follow me, one of your enemies will be immediately destroyed (and you'll get to read more of these threads, of course.)

And if you don't follow me, well, you just hurt my feelings.

😜

1. Labels

(Also referred to as "y")

The label is the piece of information that we are predicting.

For example:

- the animal that's shown in a picture
- the price of a house
- whether a message is spam or not

👇

2. Features

(Also referred to as "x")

These are the input variables to our problem. We use these features to predict the "label."

For example:

- pixels of a picture
- number of bedrooms of a house
- square footage of a house

👇

3. Samples

(This is also known as "examples.")

A sample is a particular instance of data (features or "x.") It could be "labeled" or "unlabeled."

👇

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Aditya Todmal
@AdityaTodmal

MASTER THREAD on Short Strangles.

Curated the best tweets from the best traders who are exceptional at managing strangles.

• Positional Strangles
• Intraday Strangles
• Position Sizing
• How to do Adjustments
• Plenty of Examples
• When to avoid
• Exit Criteria

How to sell Strangles in weekly expiry as explained by boss himself. @Mitesh_Engr

• When to sell
• How to do Adjustments
• Exit

1. Let's start option selling learning.
Strangle selling. ( I am doing mostly in weekly Bank Nifty)
When to sell? When VIX is below 15
Assume spot is at 27500
Sell 27100 PE & 27900 CE
say premium for both 50-50
If bank nifty will move in narrow range u will get profit from both.
— Mitesh Patel (@Mitesh_Engr) February 10, 2019

Beautiful explanation on positional option selling by @Mitesh_Engr
Sir on how to sell low premium strangles yourself without paying anyone. This is a free mini course in

Few are selling 20-25 Rs positional option selling course.
Nothing big deal in that.
For selling weekly option just identify last week low and high.
Now from that low and high keep 1-1.5% distance from strike.
And sell option on both side.
1/n
— Mitesh Patel (@Mitesh_Engr) November 3, 2019

1st Live example of managing a strangle by Mitesh Sir. @Mitesh_Engr

• Sold Strangles 20% cap used
• Added 20% cap more when in profit
• Booked profitable leg and rolled up
• Kept rolling up profitable leg
• Booked loss in calls
• Sold only

Sold 29200 put and 30500 call
Used 20% capital@44 each
— Mitesh Patel (@Mitesh_Engr) April 12, 2019

2nd example by @Mitesh_Engr Sir on converting a directional trade into strangles. Option Sellers can use this for consistent profit.

• Identified a reversal and sold puts

• Puts decayed a lot

• When achieved 2% profit through puts then sold

Already giving more than 2% return in a week. Now I will prefer to sell 32500 call at 74 to make it strangle in equal ratio.

To all. This is free learning for you. How to play option to make consistent return.
Stay tuned and learn it here free of cost. https://t.co/7J7LC86oW0
— Mitesh Patel (@Mitesh_Engr) February 7, 2020

$\uff2c\uff41\uff44\uff59 \uff22\uff4c\uff41\uff5a\uff45\uff53$

ＬａｄｙＢｌａｚｅｓ...
@Lady_Blazes

Oh my Goodness!!!

I might have a panic attack due to excitement!!

Read this thread to the end...I just had an epiphany and my mind is blown. Actually, more than blown. More like OBLITERATED! This is the thing! This is the thing that will blow the entire thing out of the water!

Tik Tok pic.twitter.com/8X3oMxvncP
— Scotty Mar10 (@Allenma15086871) December 29, 2020

Has this man been concealing his true identity?

Is this man a supposed 'dead' Seal Team Six soldier?

Witness protection to be kept safe until the right moment when all will be revealed?!

Who ELSE is alive that may have faked their death/gone into witness protection?

Were "golden tickets" inside the envelopes??

Are these "golden tickets" going to lead to their ultimate undoing?

Review crumbs on the board re: 'gold'.

#SEALTeam6 Trump re-tweeted this.

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$Ranvijay Singh\U0001f1ee\U0001f1f3$

Ranvijay Singh🇮🇳...
@ranvijayT90

#Thread Two divine forms of Khatu Shyam ji...

On the day of Amavasya, devotees get the darshan of Shyam Baba in Shyam varan (black color). In ordinary days, the form of Baba is saffron. The reason for this is a custom. The real head of Shyam Baba which had appeared from Shyam Kund is of Shyam varan.

Every day Shyam Baba's grand Shringar is done by Pujaris. The main part of Shyam Baba's shringar is Tilak. This Shringar is done by the members of Nij Mandir sevaks. The sevaks of the Nij Mandir do sandalwood tilak to Baba everyday in the auspicious time.

This Divine Tilak is applied from the forehead to the nose. When Baba is given a bath by sevaks, this tilak comes off. Then in the auspicious time, the sevaks again apply tilak to Baba.

There is no auspicious time on the Amavasya. Because of this, tilak is not done on that day. That's why devotees have darshan of Shyam Baba on the Amavasya only in the actual real form (Shyam Swaroop). Baba's tilak is done on Amavasya only on the day of Deepawali.

Suhas S. Hardutt
@HarduttSuhas

THE BATTLE OF PRATAPGARH (10 Nov, 1659)

Chhatrapati Shivaji Maharaj's impeccable military genius and matchless courage humbled the Adil Shahi Sultanate.

The beginning of what was to come later for the M0ghuls from one of the most indefatigable defenders of Dharma!

(Thread)

Shivaji’s growing influence in Maval region, made him a threat to the Adil Shahi ruler of Bijapur who sought to restrain him. The man who was given the responsibility of this mission, was their commander, Afzal Khan. Afzal was a formidable warrior known for his ruthlessness.(1)

His strategy was to bring Shivaji out into the plains, where he held an advantage, compared to the rocky Deccan terrain. Afzal attacked the holy town of Pandharpur, and later demolished the temple of Bhavani at Tuljapur to instigate Shivaji in a bid to bring him out.(2)

Shivaji was angry but remained patient. Knowing that he could not defeat Bijapuri Sultanate in an open battle, Shivaji moved to Pratapgarh fort, located on rocky heights, surrounded by thick forests of Jawali.(3)

This set the pattern for future Maratha attacks, using guerrilla warfare against larger invading armies rendering them ineffective in unfavorable terrain.(4)

WORLD OF SANATAN DHARM...
@world_sanatan

LESSER KNOWN DETAILS ABOUT THE BATTLE OF PRATAPGAD - THIS YOU ALL SHOULD READ

In the 1659 Adilshah sent Afzalkhan with the army of 75000 soldiers to destroy Chhatrapati Shivaji Maharaj with his empire. He was the main soldier, warrior and minister in Adilshahi Sultanate.

He was the Subhedar of Wai province.

He was very powerful physically. His height was 7.3 ft. due to same he was very popular as a strong man from Adilshahi Sultanate.

Whole India was not daring to fight with Adilshahi due to Afzal Khan.

Badi Begum Sahiba sent him to kill Chhatrapati Shivaji on any cost.

Voluntary he decided to destroy Chhatrapati Shivaji’s empire with him. He started his operation against Chhatrapati Shivaji from "wai". He destroyed many temples like as Tulja Bhavani Temple.

He destroyed so many villages, killed villagers to torture Chhatrapati Shivaji.

DETAILED VIEW- Running commentary

1. Chhatrapati Shivaji chose Pantaji as a lawyer to communicate with Afzal Khan. Pantaji was very sharp minded, who did his job very nicely.

2. He presented Chhatrapati Shivaji is fearing Afzal Khan and he wanted to invite him in his kingdom near Pratapgad.

3. Pantaji succeed in the communication with Afzal Khan and he brought him as per the planning of Chhatrapati Shivaji.