BC MACHINE LEARNING
Saved by @jacobhtml

Are you planning to learn Python for machine learning this year?

Here's everything you need to get started.
๐Ÿงต๐Ÿ‘‡

 In this thread, we'll look at all the concepts in Python you need to know for machine learning along with free resources to help you out.

All of this is based on my experience of successfully teaching 300+ students how to code using Python.

(2 / 19
You can use many languages for machine learning, why Python?

Because of 2 reasons:
- Comparatively easier to learn than other languages
- Has the biggest and most mature community

This makes Python a no-brainer to learn for machine learning as a beginner.

(3 / 19)
These are the absolute basics which you must know about:

- Basic terminal commands
- Basic arithmetic (+,-,/,*)
- Accepting user input
- For & While loops
- Exception handling
- If-Else statements
- Functions, modules & Imports

(4 / 19)
Then comes the more tougher concepts which you must know about:

- Object oriented programming in Python:Classes, Objects, Methods
- PIP (Pypi)
- List slicing
- String formatting
- Dictionaries & Tuples
- Managing environments
- Dunder methods like __init__

(5 / 19)
This are even more advanced concepts but you do not need then to start machine learning:

- Lambda functions
- Built in libraries like CSV, requests, Sqlite
- Map and Filter
- *args and **kwargs
- Async
- Decorators

(6 / 19)
From what I've observed, most beginners just find it really difficult just to get the Python environment setup and then using the terminal becomes an even bigger nightmare for them.

Let's tackle this issue.

(7 / 19)
You need to install:
- Anaconda for managing environments (different versions of Python)
- Python3
- Machine learning packages like Sckit learn and TensorFlow using pip when needed

(8 / 19)
Anaconda installation guide for ๐Ÿ‘‡

MacOS: ๐Ÿ”—docs.โ€‹anaconda.โ€‹com/anaconda/install/mac-os/
Windows: ๐Ÿ”—docs.โ€‹anaconda.โ€‹com/anaconda/install/windows/
Linux: ๐Ÿ”—docs.โ€‹anaconda.โ€‹com/anaconda/install/linux/

(9 / 19)
MacOS and Linux have Python pre-installed, for windows you'll have to install it yourself and it is really easy to mess up the install.

Here's a guide with step by step instructions which will help you.
๐Ÿ”—bit.โ€‹ly/3rbDoyl

(10 / 19)
After you do all of that, you need a place to write your code which is called a "code editor".

Here are some popular ones

- VS Code: Feature-rich
- Sublime: Light and simple
- Jupyter: Useful for prototyping
- Pycharm: Full-blown IDE i.โ€‹e has loads of features.

(11 / 19)
If all of that seems complicated to you, I suggest you use Google colab, Kaggle notebooks or repl.โ€‹it
These are online editors which have everything set up for you.

Not to mention colab and kaggle notebooks give you a free GPU for your machine learning workloads.

(12 / 19)
Links for these editors

Collab : ๐Ÿ”—colab.โ€‹research.โ€‹google.โ€‹com
Kaggle Notebooks : ๐Ÿ”—kaggle.โ€‹com/notebooks/welcome
Repl : ๐Ÿ”—repl. it

(13 / 19)
The Basic & Intermediate Python course on freecodecamp go over pretty much all Python concepts you need for machine learning which I have mentioned above.

Basics: ๐Ÿ”—youtube.โ€‹com/watch?v=rfscVS0vtbw
Intermediate: ๐Ÿ”—youtube.โ€‹com/watch?v=HGOBQPFzWKo

(14 / 19)
Another thing which most beginners skip is knowing how to use the terminal properly and the know-how of navigating around folders.

Here's a brilliant website which gives you an overview of the windows command prompt, enough for you to get started.

๐Ÿ”—bit.โ€‹โ€‹ly/34tmnGd

(15 / 19)
The story is a bit different on Linux and Mac, their terminals are extremely powerful and packed to the brim with features, here's a tutorial which will help you get started with the basics ๐Ÿ‘‡

โ€‹๐Ÿ”—youtube.โ€‹com/watch?v=oxuRxtrO2Ag

(16 / 19)
Keep in mind that you should learn how to use the linux terminal because at some point in your machine learning journey you will have to deal with linux.

It is not important to learn it at the start but I do recommend it.

(17 / 19)
This tutorial will help you in knowing how to work with folders, this is important!

Windows: ๐Ÿ”—youtube.โ€‹com/watch?v=HDmwiJxzIrw
Mac: ๐Ÿ”—youtube.โ€‹com/watch?v=3TAEC-1YUZw
Linux: ๐Ÿ”—youtube.โ€‹com/watch?v=HbgzrKJvDRw

(18 / 19)
That's the end of the thread!

Consider following me if you want to see more content like this, cheers.

(19 / 19)

More from Pratham Prasoon

Pratham Prasoon
Pratham Prasoon
@PrasoonPratham
These are the tools you will need for machine learning in Python.

๐Ÿงต๐Ÿ‘‡

Anaconda

When you work in python, you'll be working with several frameworks and many of them work only on specific versions of python.

(2 / 13)

Now imagine downloading a new version of python and then installing it for every framework you want to work with ๐Ÿ˜ฌ.

Meet Anaconda which allows you to run several versions of python. It comes pre-installed with several data science and machine learning frameworks.

(3 / 13)

Pip-env is also a way of maintaining several versions of Python and comes pre installed with Python.
You can use pip env or Anaconda, whichever works for you.

(4 / 13)

Jupyter Notebooks

Jupyter notebooks is an IDE just like VS code or Sublime. The special thing about jupyter is that you can parts of code in mini code editors called cells. This is great for prototyping and testing code.

(5 / 13)
MACHINE LEARNING
Pratham
Pratham
@PrasoonPratham
This is a beginner's guide on mining cryptocurrencies.

๐Ÿงต ๐Ÿ‘‡๐Ÿป


Mining 101

Typically, when you transfer money using a service like Paypal, they take a small cut for facilitating the exchange.

In cryptocurrencies, people like you and me act as Paypal and facilitate exchanges of cryptocurrency. We get a cut for this just like Paypal did.

In order to make these transactions happen, our computers need to do some calculations which requires a lot of computational power.

A GPU or a Graphics Processing Unit which is typically marketed for gaming workloads can be used to mine cryptocurrencies.

Why do you need a GPU?

Today, there are so many miners that the "difficulty" of mining cryptocurrencies has skyrocketed, which basically means it takes a lot of computational power to mine crypto which GPUs can provide and CPUs cannot.

(โ— This is an oversimplification)

If you are interested in the inner workings of how blockchain and cryptocurrency, then I highly suggest that you read this thread by @oliverjumpertz
CRYPTOCURRENCIES GUIDE
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Pratham
Pratham
@PrasoonPratham
A list of my favourite tutorials for learning Python as a beginner.

๐Ÿงต ๐Ÿ‘‡๐Ÿป

All the tutorials below include the basics like installation, variables etc.

I've also listed out the key highlights of each tutorial so that it is easy for you decide which one to pick.

Before going through these tutorials I would highly suggest you to go through this thread if you are a complete


The Full Python Course from learn with Python with Rune

key highlights
- 17 part course
- Jupyter notebooks
- Free eBook included

Duration: 8

The Python Beginner's course on FreeCodeCamp's YouTube Channel.

Key highlights

- Building a casic calculator
- Mad Libs Game
- Slightly advanced concepts like inheritance, Classes etc.

Duration: 4
ALL
Pratham Prasoon
Pratham Prasoon
@PrasoonPratham
Here's what you'll hear when you first get into machine learning.

- Neural networks
- Loss
- Weights
- Biases
- Epochs
- Neurons
- Optimizers
...

It can get very confusing really fast!

Here are some of the terms you should know about.
(I wish I had this before)

๐Ÿงต๐Ÿ‘‡


These terms won't mean anything unless you know what Machine learning is all about.

> Machine learning is the process of making a program which allows a computer to learn from data.

The data could be anything, images, audio or even text.

In machine learning we use something called a neural network, this is essentially an imitation of the human brain.

> Neural Networks are a digital imitation of the neurons you see in the human brain.


In these neural networks, data flows through them and each neuron (the circle) has a numerical value which will change.

> The value of a neuron gets changes to something which is close to what we want each time the data passes through the neural network.


Think of the neurons as dials on a lock, you have to tune every dial to open the lock.

It is almost impossible for a human to tune thousands of dials like these, but a computer certainly can.
MACHINE LEARNING
Pratham Prasoon
Pratham Prasoon
@PrasoonPratham
How to learn Python for machine learning. ๐Ÿ

The beginner's guide.
(based on my real life experience of mentoring 300+ students)

๐Ÿงต๐Ÿ‘‡

In this thread we'll look at all the concepts in Python you need to know for machine learning along with all the free resources that you'll need.

All of this is based on my experience of successfully teaching 300+ students how to code using Python.

(2 / 19)

You can use many languages for machine learning, why Python?

Because of 2 reasons:
- Comparatively easier to learn than other languages
- Has the biggest and most mature community

This makes Python a no-brainer to learn for machine learning as a beginner.

(3 / 19)

These are the absolute basics which you must know about:

- Basic terminal commands
- Basic arithmetic (+,-,/,*)
- Accepting user input
- For & While loops
- Exception handling
- If-Else statements
- Functions, modules & Imports

(4 / 19)

Then comes the more tougher concepts which you must know about:

- Object oriented programming in Python:Classes, Objects, Methods
- PIP (Pypi)
- List slicing
- String formatting
- Dictionaries & Tuples
- Managing environments
- Dunder methods like __init__

(5 / 19)
MACHINE LEARNING , PYTHON USEFUL

More from Machine learning

Pratham Prasoon
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.

๐Ÿงต๐Ÿ‘‡

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
MACHINE LEARNING
Pratham Prasoon
Pratham Prasoon
@PrasoonPratham
Machine learning terms you must know about as a beginner.

๐Ÿงต๐Ÿ‘‡

These terms won't mean anything unless you know what Machine learning is all about.

> Machine learning is the process of making a program which allows a computer to learn from data.

The data could be anything, images, audio or even text.

(2 / 11)

In machine learning we use something called a neural network, this is essentially an imitation of the human brain.

> Neural Networks are a digital imitation of the neurons you see in the human brain.

(3 / 11)


In these neural networks, data flows through them and each neuron (the circle) has a numerical value which will change.

> The value of a neuron gets changes to something which is close to what we want each time the data passes through the neural network.

(4 / 11)


Think of the neurons as dials on a lock, you have to tune every dial to open the lock.

It is almost impossible for a human to tune thousands of dials like these, but a computer certainly can.

(5 / 11)
MACHINE LEARNING
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Ram Bhupatiraju
Ram Bhupatiraju
@RamBhupatiraju
Happy 2โƒฃ0โƒฃ2โƒฃ1โƒฃ to all.๐ŸŽ‡

For any Learning machines out there, here are a list of my fav online investing resources. Feel free to add yours.

Let's dive in.
โฌ‡๏ธโฌ‡๏ธโฌ‡๏ธ

Investing Services

โœ”๏ธ @themotleyfool - @TMFStockAdvisor & @TMFRuleBreakers services

โœ”๏ธ @7investing

โœ”๏ธ @investing_city
https://t.co/9aUK1Tclw4

โœ”๏ธ @MorningstarInc Premium

โœ”๏ธ @SeekingAlpha Marketplaces (Check your area of interest, Free trials, Quality, track record...)

General Finance/Investing

โœ”๏ธ @morganhousel
https://t.co/f1joTRaG55

โœ”๏ธ @dollarsanddata
https://t.co/Mj1owkzRc8

โœ”๏ธ @awealthofcs
https://t.co/y81KHfh8cn

โœ”๏ธ @iancassel
https://t.co/KEMTBHa8Qk

โœ”๏ธ @InvestorAmnesia
https://t.co/zFL3H2dk6s

โœ”๏ธ

Tech focused

โœ”๏ธ @stratechery
https://t.co/VsNwRStY9C

โœ”๏ธ @bgurley
https://t.co/NKXGtaB6HQ

โœ”๏ธ @CBinsights
https://t.co/H77hNp2X5R

โœ”๏ธ @benedictevans
https://t.co/nyOlasCY1o

โœ”๏ธ

Tech Deep dives

โœ”๏ธ @StackInvesting
https://t.co/WQ1yBYzT2m

โœ”๏ธ @hhhypergrowth
https://t.co/kcLKITRLz1

โœ”๏ธ @Beth_Kindig
https://t.co/CjhLRdP7Rh

โœ”๏ธ @SeifelCapital
https://t.co/CXXG5PY0xX

โœ”๏ธ @borrowed_ideas
MACHINE LEARNING , ALL
Kieran Snyder
Kieran Snyder
@KieranSnyder
1 There's a chasm between an NLP technology that works well in the research lab and something that works for applications that real people use. This was eye-opening when I started my career, and every time I talk to an NLP engineer at @textio, it continues to strike me even now.

2 Research conditions are theoretical and/or idealized. A huge problem for so-called NLP or AI startups with highly credentialed academic founders is that they bring limited knowledge of what it takes to build real products outside the lab.

3 A product is ultimately a thing that people pay for - not just cool technology or user experience. But Iโ€™m not even talking about knowledge gaps in go-to-market work. I'm talking purely technical gaps: how you go from science project to performant + delightful user experience.

4 Most commoditized NLP packages solve well-understood problems in standard ways that sacrifice either precision or performance. In a research lab, this is not usually a hard trade-off; in general, no one is using what you make, so performance is less important than precision.

5 In software, when youโ€™re making something for real people to use, these tradeoffs are a big deal. Especially if youโ€™re asking those people to pay for what youโ€™ve made (canโ€™t get away from that pesky GTM thinking). They expect quality, which includes precision AND performance.
MACHINE LEARNING
Pratham Prasoon
Pratham Prasoon
@PrasoonPratham
A simple guide on how to train your first neural network in under 10 minutes to kick-start your machine learning journey.

(+ no setup required and free extra resources)
๐Ÿงต๐Ÿ‘‡

For this exercise, you'll need :
- A computer/phone
- An internet connection
- Very basic Python knowledge
- Willingness to learn

What you do not need:
- Complex math
- An expensive computer
- A PhD

Math will come later on in your machine learning journey :)

(2 / 15)

Here's our problem,we are given data in which we are given the number of flats in a house and its corresponding price. Like a house with one flat is worth 10000, and 20000 for a house with 2 flats.

(3 / 15)


We can clearly tell that the price of the house increases by 10000 per extra flat however our computer does not know this and we won't it tell it about this, it'll have to figure things out on its own.

(4 / 15)

Here's the code๐Ÿ‘‡

(5 / 15)
MACHINE LEARNING

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RISHI ASHTAVAKRA(DEVAL)

Those who are well versed in our ancient scriptures must definitely have come across the name of Ashtavakra Muni. Who was he and what is the story behind his body getting crooked from all sides.

Muni Asit was the son of Pracheta Rishi.


He was childless and this caused a lot of worry to him. But an Akashvani assured him that he should go to Shivji to get all his
worries nullified.

So he along with his wife go to Shivji and after paying him obeisance start singing Shivji's praises.

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Shiv gets satisfied by them soon and is ready to grant a boon.

Shiva told him that, he very well knew what the Muni desired. He then gives him the "Sansaar Vijay' mantra and asks him to keep reciting it. The Isht devi would come at an appropriate time and fulfill his wish.

Asit Muni kept reciting this mantra for hundred years. The Devi appeared and granted him the boon of a son as a Shivansh. A son was born and was named as Deval. He grew upto be a great scholar well versed and a handsome young man. Asit Muni got him married to Ratnamalavati.
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THE UNTOLD TALE OF THE LESSER KNOWN WARRIORS:JOGRAJ GURJAR & RAMPYARI GURJAR AND HOW DID THEY DEFEAT THE CRUELEST ISLAMIC INVADER, TIMUR LANG.

Timur Lang, was the founder of Timurid Empire. He was born in April 1336 CE, somewhere in central asia.


During his reign, he conquered Persia, Egypt, Central Asia and emerged as the most powerful and heinous ruler. He was a religious fanatic like the rest of the Islamic invaders who tried to invade India.

Timur Lang decided to invade India in 1398 as he heard about India's splendid wealth and had a desire to conquer India which his ancestor Genghis Khan couldn't do.


After Firoz Shah's demise in1388, Delhi Sultanate was in a state of political turmoil.During this time, Timur sent his grandson Pir Mohammad to attack Indian front. He invaded Multan&was able to defeat Iqbal Khan's brother but when he met with resistance,he asked help from Timur.


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DODDA GANAPATHI, BENGALURU
#karnatakatemples

Dodda in Kannada means big. Sri Ganesha here is 18 ft tall in height and 16 ft wide. Dodda Ganesha is also known as Shakthi Ganapathi and Satya Ganapathi.

It is said that Kempe Gowda, the founder of Bengaluru, saw a rock here.


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Benne Alankara is the most famous festival here when about 100 kg of butter is coated over Ganapathi every four years and the rich prasad is offered to the worshippers.

Ganesha is also covered in cream colour and is decorated with golden laces and buttons
@GampaSD


The Dodda Ganapathi murti is believed to be still growing in size, especially towards right.

The weavers from old Bengaluru tie a coconut to their looms before starting work. When the work is completed, they break 100 coconuts here along with the one tied on the loom.
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Simple and effective way 2 make Money


Idea 1:- Use pivot level like 14800 in case of nifty and sell 14800straddle monthly expiry (365+335) exit if nifty closes on daily basis below S1 or above R1

After closing below S1 if it closes above S1 next day or any day enter the same position again vice versa for R1

Idea2:- Use R1 and S1 corresponding strikes multiple
Incase of R1 15337 take 15300ce
N in case of S1 14221 use 14200pe
Sell both and hold till expiry or exit if nifty closes below S1 or above R1 around closing
If the same bounces above S1 and falls below R1 re-enfer same strikes

Use same criteria for nifty, usdinr and banknifty

(This is must)Use this margin rule for 1lot banknifty pair keep 4Lax margin
For nifty one lot keep 3Lax
For usdinr 100lots keep 4Lax

I bet you if you do this on consistent basis your ROI will be more than 70% on yearly basis.

Couldn't explain easier than this

Criticisms are most welcomed.
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Pieter Levels \u2728
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Joe Rogan's podcast is now is listened to 1.5+ billion times per year at around $50-100M/year revenue.

Independent and 100% owned by Joe, no networks, no middle men and a 100M+ people audience.

๐Ÿ‘

https://t.co/RywAiBxA3s

Joe is the #1 / #2 podcast (depends per week) of all podcasts

120 million plays per month source https://t.co/k7L1LfDdcM


https://t.co/aGcYnVDpMu
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