BC MACHINE LEARNING
Saved by @Mollyycolllinss

Which libraries do you really need to get started with Machine Learning and why?

🧵👇

 First and foremost, make sure that you have nailed the fundamental concepts of Python because machine learning requires a lot of programming!

(2 / 19)
If you know these topics, then you are good to go with machine learning in Python

- Object-oriented programming in Python:Classes,Objects,Methods
- Lists & List functions
- List comprehension
- List slicing
- String formatting
- List,Dictionaries & Tuples

(3 / 19)
Now, let's understand what popular python machine learning libraries do.

We will talk about👇
- TensorFlow (+ Keras)
- PyTorch
- Pandas
- Numpy
- Matplotlib
- SciKit Learn
- Seaborn

(4 / 19)
Now let's cover the libraries that you have to learn (at least the basics) for machine learning

1. Pandas

Pandas is a python library that allows you to store and read data from spreadsheets ( .csv, .xlsv files ) in structures called Dataframes.

(5 / 19)
Dataframes allow you to work with data present in a spreadsheet.

Pandas help you make the data frame itself.

(6 / 19)
2. Numpy

Numpy allows you to manipulate the data. It replaces python lists and does the same things, like list slicing for example. However numpy lists are much faster to execute than the default python lists.

(7 / 19)
3. Matplotlib

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

(8 / 19)
There is another library based on Matplotlib which is called Seaborn.

Seaborn is based on Matplotlib and allows you to visualize data with support for themes (as in color schemes like VS code themes) and more visualization options.

(9 / 19)
It is definitely not mandatory to learn Seaborn at the beginning of your machine learning journey.

Use it when you need to.

(10 / 19)
Why is it important to learn these libraries?

In machine learning, you will have to work with a lot of messy data! A lot!
These libraries are essential for you so that you can manipulate and analyze data.

(11 / 19)
You must always remember that data largely dictates how well your machine learning model will perform.

Do not ignore data analysis and cleaning.

It is even more important than neural network!

(12 / 19)
Now we are left with TensorFlow, PyTorch, and Scikit Learn.

Let's talk about them.

(13 / 19)
TensorFlow allows you to make neural networks, anything from computer vision to natural language processing, TensorFlow will help you out. Keras allows you to make machine learning models and is heavily integrated with Tensorflow v2 (the latest version of TensorFlow).

(14 / 19)
- PyTorch does the same things as TensorFlow in a different syntax.

- Both PyTorch and TensorFlow are equally amazing libraries.

(15 / 19)
SciKit Learn

Scikit learn does a lot of things, from regression to classification, you name it.
It is a great tool to have when working on machine learning.

(16 / 19)
Let's summarize.

Step 1: Learn Python well.
Step 2: Learn the basics of Numpy, Pandas, and matplotlib.
Step 3: Learn either PyTorch or TensorFlow or SciKit learn at the start.

(17 / 19)
Step 4: Do a project from Kaggle where you can use these tools. (Pro-tip, look at notebooks and solutions from other people, this will help you in understanding how experienced people tackle certain problems)

(18 / 19)
The best tutorials to learn these would be from the freecodecamp YouTube channel, simply search for `framework name` freecodecamp.

They have some of the best machine learning tutorials out there.

(19 / 19)

More from Pratham Prasoon

Pratham
Pratham
@PrasoonPratham
This is a beginner's guide on mining cryptocurrencies.

🧵 👇🏻


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.

(❗ 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
Pratham Prasoon
Pratham Prasoon
@PrasoonPratham
There are several skills needed for learning machine learning that no one talks about.

Here are some of them.
(from what I've learned over the past two years)
🧵👇

This thread aims to introduce to you some of the skills often ignored for learning machine learning but are actually very important.

These skills will enable you to learn concepts quickly and more efficiently in this field.

(2 / 9)

1⃣ Reading

Probably one of the most underrated skills on this list.

In machine learning, you HAVE to read a lot of articles, papers, documentation, and whatnot.

It is mostly due to the theoretical nature of this field that reading is an important skill to have.

(3 / 9)

2⃣ Strong fundamentals in programming

Machine learning is just a lot of programming mixed with math and data.

Having clear programming fundamentals is crucial for this field.

I highly suggest you learn about these if you are using Python for machine learning.👇

(4 / 9)

- Object oriented programming in Python :Classes, Objects, Methods
- Lists & List functions
- Dunder Methods
- List comprehension
- List slicing
- String formatting
- List, Dictionaries & Tuples
- *args,**kwargs

(5 / 9)
MACHINE LEARNING
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Pratham Prasoon
Pratham Prasoon
@PrasoonPratham
Machine Learning for the Web developer in 2021.

The beginner's guide.​

🧵👇

I started machine learning as a web developer, if I can do it then anyone can.

This carefully curated thread will give you key insights into my journey and how you can make this transition, seamlessly.

(2 / 14)

"Machine learning is not what you think it is"

One of the main reasons why people find it difficult to get started with machine learning is because of the lack of information, and rightfully so.

(3 / 14)

Machine learning as a concept has existed since the 1950s, but has only become popular in recent years because of the exponential rise of advancements in computer hardware.

(4 / 14)

In short, it because of the sudden rise of this technology ,which was previously unknown to the general public, that there is a lot of misinformation around it.

(5 / 14)
MACHINE LEARNING
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
Pratham
Pratham
@PrasoonPratham
A git cheat sheet for all your needs!

https://t.co/H7QdOrthwV
GIT

More from Machine learning

Pratham Prasoon \U0001f680
Pratham Prasoon 🚀...
@PrasoonPratham
Here's how I got started with my first machine learning project and you can too.
Let's take a look.

(This thread will take from zero to being a hero in machine learning, trust me)

(1 / 22)
🧵👇


Getting started with your first machine learning project might actually much easier than it seems, if I can do it, certainly anyone can.

I did not use:
- Any Math
- An expensive computer
- Complex programming concepts

(2 / 22)

Here's what I did use:

- A free GPU on Google Colab
- Python
- TensorFlow
- Numpy
- Pandas
- Kaggle
- Scikit-Learn
- Google
- StackOverFlow

(3 / 22)

This project was actually a Kaggle challenge based on the MNIST dataset which is a collection images of 70,000 hand written digits.

You can find the dataset here👇
🔗//kaggle.com/c/digit-recognizer

(4 / 22)


Before we go over the code of this project, it is highly reccomended that you complete this free course on YouTube👇

Machine Learning foundations course
🔗//youtu.be/_Z9TRANg4c0

Code👇
🔗//colab.research.google.com/github/PrasoonPratham/Kaggle/blob/main/MNIST.ipynb

(5 / 22)
MACHINE LEARNING
Madison Kanna
Madison Kanna
@Madisonkanna
With hard work and determination, anyone can learn to code.

Here’s a list of my favorites resources if you’re learning to code in 2021.

👇

1. freeCodeCamp.

I’d suggest picking one of the projects in the curriculum to tackle and then completing the lessons on syntax when you get stuck. This way you know *why* you’re learning what you’re learning, and you're building things

2. https://t.co/7XC50GlIaa is a hidden gem. Things I love about it:

1) You can see the most upvoted solutions so you can read really good code

2) You can ask questions in the discussion section if you're stuck, and people often answer. Free

3. https://t.co/V9gcXqqLN6 and https://t.co/KbEYGL21iE

On stackoverflow you can find answers to almost every problem you encounter. On GitHub you can read so much great code. You can build so much just from using these two resources and a blank text editor.

4. https://t.co/xX2J00fSrT @eggheadio specifically for frontend dev.

Their tutorials are designed to maximize your time, so you never feel overwhelmed by a 14-hour course. Also, the amount of prep they put into making great courses is unlike any other online course I've seen.
MACHINE LEARNING
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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
Anson Horton
Anson Horton
@AnsonHorton
1/ It's probably not the first thing you think of, but when we started .NET (COM+) in the late 90s, C# didn't exist yet. We were working on it at the same time as the CLR and the framework. So, you might wonder, what language was being used to generate IL and write the BCL?

2/ The answer is a language that we called SMC that Peter Kukol wrote the compiler for. Peter is a flat out amazing engineer and wrote the core parts of the compiler in just a few days. This unblocked the framework team, allowed vetting the runtime and interpreter, etc.

3/ SMC was a trimmed down C++ variant and the compiler was written in itself (i.e. SMC). It didn't support things like destructors, multiple inheritance, virtual base classes, etc. But, overall it enabled progress that would have otherwise been stalled.

4/ It did introduce a problem for the C# language and compiler team though. Every day more and more code was written in SMC, a language that we did not intend to ship externally. Designing a new language is hard (well, at least, a general purpose one that becomes popular).

5/ So there was a constant balancing act between laying enough of the foundation of the language and the C# compiler to replace SMC and making sure that the design lived up to the goals and ideals the team was striving for. If we waited too long to adopt C#,
MACHINE LEARNING
Pratham Prasoon
Pratham Prasoon
@PrasoonPratham
Do you want to learn the maths for machine learning but don't know where to start?

This thread is for you.
🧵👇

The guide that you will see below is based on resources that I came across, and some of my experiences over the past 2 years or so.

I use these resources and they will (hopefully) help you in understanding the theoretical aspects of machine learning very well.

Before diving into maths, I suggest first having solid programming skills in Python.

Read this thread for more


These are topics of math you'll have to focus on for machine learning👇

- Trigonometry & Algebra

These are the main pre-requisites for other topics on this list.

(There are other pre-requites but these are the most common)

- Linear Algebra

To manipulate and represent data.

- Calculus

To train and optimize your machine learning model, this is very important.
MACHINE LEARNING

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Patrick McKenzie
Patrick McKenzie
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A thread on HN about bad code in legacy projects both makes me think how little we've learned as a discipline over the years and, honestly, how little credit we give ourselves for some pretty major

Fun going down this list and thinking: "Hmm, plausible at a well-run modern software shop", "Hmm, possible, but requires implausible tradeoffs", "Literally disallowed by languages", and "If you were to attempt doing that our test suite wouldn't let you merge."

I think we as an industry celebrate (not quite the right word) failure too much and don't celebrate success nearly enough. There is no DailyWTF for competent execution, word of which generally stays pretty local to the source while incompetence passes into legend.

Alrighty let me try to thread the needle on being the change I want to see in the world while not giving away anything that will get me in trouble:

Ruby has wonderful developer ergonomics. Typed languages are easier for machines to guarantee the correctness of. We built a type checker for Ruby (and I believe it is slated for OSS release sometime).

c.f.
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Sheetal Rijhwani
Sheetal Rijhwani
@RijhwaniSheetal
How you can filter stocks for Intraday trades - 🧵

As a kid, we would do homework before school the next day - you have to do homework here too. A specific sector performs on a particular day and studying things a day before will help you spot that particular sector. (1/11)

To prepare for the next trading day, you got to check the Index charts of all sectors. You got to keep an eye on a specific Index if you see a few factors:

- If any Index closed exactly at the resistance, you can get a trade on either side on the next day. (2/11)

- If it gave a rejection candle at resistance.

- If any Index closed exactly at support, you can get a trade on either side the following day.

- If there was a bullish signal at the support.

- If any Index gave a breakout or breakdown and then gave a good closing. (3/11)

- If any Index has taken support at 50 or 200 EMA.

Once you spot the Index, look at all the stocks in that particular Index. Pick at least 3-4 stocks from that Index and add them to your watchlist for the next day. It's ideal to check both higher and lower timeframes. (4/11)

What you should do next day? (When I am unable to check Indices and stocks a day before, I sometimes directly start with this process.)

- Start with checking the top gainers/losers list. I observe market from 9:15 to 9:45am to check the sectors performing well on that day.(5/11)
SCREENERS , ALL , INTRADAY STRATEGIES
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Stephen Fasenfeld \U0001f600
Stephen Fasenfeld 😀...
@fasenfeld
1. “Who Are Ireland’s Members Of The World Economic Forum Implementing The Great Reset In 2021 ? 🧵1/28

🕵️🔎 Politicians, Musicians, Clergy, Bankers, Journalists, Human Rights, Artificial Intelligence, Sustainability, Corporations & Agriculture


2. In previous threads I have looked at 🇮🇪 key players in the Trilateral Commission as well as 🇮🇪 people who have attended the Bilderberger meetings. I also looked at the 🇮🇪 in the Young Global Leaders which is a site run by the WEF. You will see a lot of crossover between them.

3. The purpose of this thread is to compile all the Irish members into one thread for ease of access and reading rather than having to dive through the WEF site. This is by no means definitive and I will be adding more over time when they pop out of the woodwork.

4. Ireland’s WEF members are drawn from all walks of life but can be split into 2 separate areas. The WEF calls the first category “people” and the 2nd one “agenda contributors”. In total Ireland has a minimum of 61 members. Some of the names will be familiar and others will not

5. In this thread I’m just going to concentrate on the 21 people (there are probably a lot more). There are also 40 “agenda contributors” which I will cover in a separate thread. The agenda contributors are those who write articles or do research for the WEF site.
ECONOMY
sriram
sriram
@SriramKannan77
12 Azhwars (Alwars)

The 12 Azhwars, (the Main composers of Naalayira Divya Prabandam - 4000 poetic Rhymes) are known as Nityasuris or Divyasuris; those who reside permanently with the Lord in His Heavenly abode.


They have incarnated as Azhwars -Vasihnavite Saints - to propagate Bhakthi Marga, devotion to Lord Vishnu.

Alwars have dedicated their lives towards Lord Sriman Narayana and they led their life by praising Him.

These Alwars were born in various parts of our country but they were all united in a common subject, ie dedicating their lives and praising the Lord.

Poigai Alwar, Bhoodath Alwar and Pei Alwar are collectively called as "Mudhal Alwars" were found in the same period of time.

Thirumazhisai Alwar, who thought the Perumal as his friend and has sung Mangalasasanam on Perumal.

Thirumangai Alwar was a great king and later, changed his life to work for the Perumal and for his bhaktaas.

Thondaradippodi Alwar, who is also named as "Vipra Narayanar" led his life as the servant to the God and did service to Him by offering beautiful Garlands.
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Maurice Casey
Maurice Casey
@MauriceJCasey
I am reading through reams of late-1920s communist poetry and easily my favourite title so far is: "To a Lady who Rejected a Poem about Spring as a Petit-Bourgeois Deviation"

The last lines:
"So here's my hat into the air,
Three cheers for your amazing hair,
For coal mines, and for turbines, too,
For steel, the Comintern and you!"

A not exactly graceful (though possibly satirical) title: "Lines Disassociating Myself from Yessenin and Supporting the Otherwise Unfounded Legend that I am a Foremost Proletarian Writer"

An excerpt:
"Goodbye verses of Yessenin
Goodbye literary slop-
You are not the line of Lenin
You are not the line of WAPP

Never shall I moan a
simple lyric from the heart
I'll devote my new corona
to the proletarian art"

The poet was Joseph Freeman, who published much of his revolutionary verse in the New Masses, a stylish journal of the interwar American literary Left.
CULTURE