BC MACHINE LEARNING
Saved by @Alex1Powell

These are the tools you will need for machine learning in Python.

🧵👇

 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 😬.

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)
Google Collab

Collab is a jupyter notebook running on google's servers which gives you access to GPUs and TPUs for training machine learning models faster for free, yes free.

(6 / 13)
Kaggle

I like to call Kaggle the codepen for machine learning and data science.This is the place where you show off you machine learning skills. You have access to datasets for which you can make machine learning models and compete with other people around the world.

(7 / 13)
TensorFlow

TensorFlow is a framework for machine learning,it has variants like TensorFlow.js for machine learning in the browser, TensorFlow lite for machine learning on mobile phones, and the standard TensorFlow library.

(8 / 13)
PyTorch

PyTorch is an open-source machine learning library based on the Torch library,used for applications such as computer vision and natural language processing. It is very similar to TensorFlow in the things you can do in it with differences in the syntax.

(9 / 13)
Matplotlib

Matplotlib is a library for plotting data into pie charts, bar charts, and whatever kinds of graphs you can imagine.

(10 / 13)
NumPy

Numpy replaces the lists in Python with its lists, but why? Aren't the default lists good enough? The thing is that NumPy lists are much faster than Python lists, hence the wide usage of NumPy.

(11 / 13)
SciKit Learn

SciKit learn is a machine learning library that features various classification, regression, and clustering algorithms including support vector machines. These are complex computations you may need in training your machine learning model.

(12 / 13)
This thread took over 3 hours to make, your support by following me if you like this content will be highly appreciated! 🙏🔥

Stay tuned for more threads, good luck in your machine learning journey.

(13 / 13🎉)

More from Pratham Prasoon

Pratham
Pratham
@PrasoonPratham
A list of my favourite tutorials for learning Python as a beginner.

🧵 👇🏻

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

Here's everything you need to get started.
🧵👇

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)
MACHINE LEARNING , PYTHONML
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Pratham Prasoon
Pratham Prasoon
@PrasoonPratham
How to learn Python for machine learning. 🐍

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

🧵👇

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
Pratham
Pratham
@PrasoonPratham
I remember how hard it was to find resources for machine learning a couple of years ago.

Now we have so many of them, its impossible for you to not find what you're looking for.

Here are some of them 🧵

1️⃣ Kaggle

My go to place for finding datasets and code snippets, also a great place for discussions.


2️⃣ Papers with code

A collection of the latest and greatest research papers and the best part, a github repository with the code is included!


3️⃣ Kdnuggets

Guides, tutorials and articles on everything AI, Analytics, Big Data, Data Mining, Data Science, and Machine Learning.


4️⃣ Towards Data Science

A blog about data science with accessible and well written articles.
CODING
Pratham
Pratham
@PrasoonPratham
There are a million machine learning tutorials and courses out there which makes it really hard to choose the right one for you.

Here are some that I really like that you might find useful.
🧵 👇🏻

Before we begin, here's some unsolicited advice.

It is not about how many courses you take that matters but the problems that you solve with your knowledge.

You can take any combination of courses and tutorials from this list that you think will be useful.

Python crash course by Traversy Media (YouTube)

• Introduces you to a lot of basic concepts in Python
• Only 1.5 hours long


Intermediate Python course (YouTube)

• Helps you understand some of the more difficult concepts in Python


The neural networks series by 3blue1brown (YouTube)

• Visually explains neural networks
• Short and concise
ALL

More from Machine learning

Ram Bhupatiraju
Ram Bhupatiraju
@RamBhupatiraju
Happy 2⃣0⃣2⃣1⃣ to all.🎇

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.
⬇️⬇️⬇️

Investing Services

✔️ @themotleyfool - @TMFStockAdvisor & @TMFRuleBreakers services

✔️ @7investing

✔️ @investing_city
https://t.co/9aUK1Tclw4

✔️ @MorningstarInc Premium

✔️ @SeekingAlpha Marketplaces (Check your area of interest, Free trials, Quality, track record...)

General Finance/Investing

✔️ @morganhousel
https://t.co/f1joTRaG55

✔️ @dollarsanddata
https://t.co/Mj1owkzRc8

✔️ @awealthofcs
https://t.co/y81KHfh8cn

✔️ @iancassel
https://t.co/KEMTBHa8Qk

✔️ @InvestorAmnesia
https://t.co/zFL3H2dk6s

✔️

Tech focused

✔️ @stratechery
https://t.co/VsNwRStY9C

✔️ @bgurley
https://t.co/NKXGtaB6HQ

✔️ @CBinsights
https://t.co/H77hNp2X5R

✔️ @benedictevans
https://t.co/nyOlasCY1o

✔️

Tech Deep dives

✔️ @StackInvesting
https://t.co/WQ1yBYzT2m

✔️ @hhhypergrowth
https://t.co/kcLKITRLz1

✔️ @Beth_Kindig
https://t.co/CjhLRdP7Rh

✔️ @SeifelCapital
https://t.co/CXXG5PY0xX

✔️ @borrowed_ideas
MACHINE LEARNING , ALL
Pratham Prasoon
Pratham Prasoon
@PrasoonPratham
Are you planning to learn Python for machine learning this year?

Here's everything you need to get started.
🧵👇

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)
MACHINE LEARNING , ALL , PYTHON STUDY
Advertisement
Pratham Prasoon
Pratham Prasoon
@PrasoonPratham
Machine learning terms you must know about as a beginner.

🧵👇

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
Fionna O'Leary, \U0001f56f\U0001f1ea\U0001f1fa
Fionna O'Leary, 🕯🇪🇺...
@fascinatorfun
Thanks for this incredibly helpful analysis @dgurdasani1

Two questions. 1/ Does this summarise the AZ published data :
The plan is to extend the time interval for all age groups despite it being largely untested on the over 55yrs, although the full data is not yet published


Do we have the actual numbers of over 55yr olds given a 2nd dose at c12 weeks and the accompanying efficacy data?

Not to mention the efficacy data of the full first dose over that same period?

I’d quite like to know whether I am to be a guinea pig & the ongoing risks to manage

You attached photos of excerpts from a paper. Could you attach the link?

Re Pfizer. As I understand it the most efficacious interval for dosing was investigated at the start of the trial.


Here’s the link to the

I’ve got to say that this way of making and announcing decisions is not inspiring confidence in me and I am very pro vaccination as a matter of principle, not least because my brother caught polio before vaccinations available.
MACHINE LEARNING
Santiago
Santiago
@svpino
The 8-step quick-start guide to learn Machine Learning.

🧵👇

1⃣ Start with Python 🐍

Yes, you can do other languages, but Python is by far the most straightforward option.

👇

2⃣ Get familiar with numpy, pandas, and matplotlib

These three libraries are probably the most common Python libraries you'll have to use every day.

(Even if you don't end up doing machine learning, these libraries are awesome and useful.)

👇

3⃣ Start using notebooks

Look into Jupyter or Google Colab.

Notebooks are essential for data scientists and machine learning practitioners. Most of the code you'll read and write will be in notebooks.

👇

4⃣ Find a problem (already solved)

In my opinion, the best way to start is by working through a problem —especially when you can learn from its solution.

Start with something simple. I usually recommend "Titanic" from Kaggle.

👇
MACHINE LEARNING , STARTING MACHINE LEARNING

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The Straddle is my favorite trade these days. But more often than not, one needs to adjust to make a good return from it. There are many ways to do this depending on your view, psychology and imagination. Will discuss a few that I use

The Straddle is a delta neutral strategy and it is important to keep deltas in check during the course of the Trade and not let them run away from you. Of course Volatility is the elephant in the room- but not going there as that will require a separate discussion

The first and most common method is to go 'inverted.' That is to say to shift the untested side to generate additional Premium and increase the runway to Breakeven. So if you start with a Straddle at 700 and price goes to 740, you can move the PE up to say 720.

How much should you move the untested side? While again this is a matter of skill, experience and artistry, the rule of thumb is to cut deltas by half. Remember, prices will move and you have to allow sufficient room for them to fluctuate

The second method is to shift the entire Straddle in the direction of the move. So if you have a 700 Straddle and Price goes to 740, you can shift the Straddle to 720. If you time this right, it can generally be done at virtually no cost or minimal cost.
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Billy Bostickson \U0001f3f4\U0001f441&\U0001f441 \U0001f193
Billy Bostickson 🏴👁&👁 ...
@BillyBostickson
@EricTopol @NBA @StephenKissler @yhgrad B.1.1.7 reveals clearly that SARS-CoV-2 is reverting to its original pre-outbreak condition, i.e. adapted to transgenic hACE2 mice (either Baric's BALB/c ones or others used at WIV labs during chimeric bat coronavirus experiments aimed at developing a pan betacoronavirus vaccine)

@NBA @StephenKissler @yhgrad 1. From Day 1, SARS-COV-2 was very well adapted to humans .....and transgenic hACE2 Mice


@NBA @StephenKissler @yhgrad 2. High Probability of serial passaging in Transgenic Mice expressing hACE2 in genesis of SARS-COV-2


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https://t.co/9Z4oJmkcKj


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Ankur Patel
Ankur Patel
@AnkurPatel59
FEW USEFUL CHARTINK SCANNERS :

This Scanner will be More than Enough for any Swing / Positional Trader to find Good Stocks.

Few Important Chart ink Scanners which can help you to find Setups and boost up your Trading Account : 🧵

Do Retweet If you If it Useful

1. Momentum Stocks

This Scanner can be helpful for Short term Swing Trades . you can easily find Stocks which are in Momentum.

Stocks which are up by 20 % in 5 Days
Stocks which are up by 30 % in 30 Days
Stocks which are up by 30 % in 30 Days

2. Volume Spike

We always Look for Price and Volume Confirmation for Breakout Trading. This scanner will help you to find the Stocks Which has given a move with Good Volume . You can keep this Stock in Radar and catch the Next move in them :

3 . Reversal Scan

Stocks which are Down by 20 % + in 5 days

When Market Corrects for sometime , You can find Stocks where selling is Exhausted and they can make a Good Reversal move .

4. Reversal Scan - Stock Down continues 5 days .

If you Know the Concept of 3-5 Days move . you might catch few stocks from this scanner. Always try and Pick stocks which are in strong momentum , you have more Chances of Getting Right in Reversal Trade.
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sriram \U0001f1ee\U0001f1f3
sriram 🇮🇳...
@SriramKannan77
Saptapadi

The most essential rite (Sanskrit: rītī) of a Hindu marriage ceremony is Saptapadi (English: seven steps, saptapadī). Saptapadi is a Sanskrit term that means "seven steps." The wedded couple walks seven steps after tying the Mangalsutra, which is known as Saptapadi.


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Hindu wedding rituals: During Saath Phere, the bride and groom exchange their marital vows while circumambulating (walking around) the sacred fire seven times.


This is because the bride and groom exchange marriage vows and hope for a happy married life together.

1. The first marriage vow, out of seven, is a petition to the Lord for nutrition and nourishment for the Hindu bride and groom.


2. Hindu Bride and Groom: The second marriage promise is for strength in sickness, health, good times, and terrible times.

3. For the Hindu Bride and Groom - The third marriage promise is for the Hindu bride and groom's prosperity.

4. For Hindu Bride and Groom - The fourth pledge is to stand by their families in good times and bad.
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This Scanner will be More than Enough for any Swing / Positional Trader to find Good Stocks.

Few Important Chart ink Scanners which can help you to find Setups and boost up your Trading Account : 🧵

Do Retweet If you If it Useful

1. Momentum Stocks

This Scanner can be helpful for Short term Swing Trades . you can easily find Stocks which are in Momentum.

Stocks which are up by 20 % in 5 Days
Stocks which are up by 30 % in 30 Days
Stocks which are up by 30 % in 30 Days

2. Volume Spike

We always Look for Price and Volume Confirmation for Breakout Trading. This scanner will help you to find the Stocks Which has given a move with Good Volume . You can keep this Stock in Radar and catch the Next move in them :

3 . Reversal Scan

Stocks which are Down by 20 % + in 5 days

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4. Reversal Scan - Stock Down continues 5 days .

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