BC MACHINE LEARNING

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Pratham Prasoon
@PrasoonPratham 3 years, 3 months ago 773 views

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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

.. and instead creates a copy of the csv or database as a python array in the RAM.

We use Numpy in order to change the shape of the data we just pulled using Pandas, the arrays are converted to special numpy arrays which are much faster than Python's default ones.

(6 / 11)

Matplotlib is used to visualize data, bar graphs, line plots, pie charts... you get the point.

These are the frameworks that you will have to use extensively throughout your machine learning journey.

(7 / 11)

Now comes the tougher part, TensorFlow, Pytorch, Keras or Scikit learn? Or all? or neither?....

It depends on personal preference.

(8 / 11)

TensorFlow, Keras and Pytorch generally do the same thing in different syntax.

SciKit learn has a bunch of scientific tools which you might need here and there.

(9 / 11)

Here's what I suggest, try making a convolutionary neural network for the MNIST dataset with all of these and then decide which one is right for you.

I personally use TensorFlow + ScikitLearn, it might not work for you.

(10 / 11)

More from 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)

Pratham Prasoon
@PrasoonPratham

Everything you need to know about the math for machine learning as a beginner.

🧵👇

Before diving into the math, I suggest first having solid programming skills.

For example👇

(2 / 17)

In Python, these are the concepts which you must know:

- Object oriented programming in Python : Classes, Objects, Methods
- List slicing
- String formatting
- Dictionaries & Tuples
- Basic terminal commands
- Exception handling

(3 / 17)

If you want to learn python, these courses are freecodecamp could be of help to you.

🔗Basics: youtube .com/watch?v=rfscVS0vtbw
🔗Intermediate :youtube .com/watch?v=HGOBQPFzWKo

(4 / 17)

You need to have really strong fundamentals in programming, because machine learning involves a lot of it.

It is 100% compulsory.

(5 / 17)

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Pratham Prasoon
@PrasoonPratham

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)

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

What actually is a Blockchain?

Bitcoin is breaking record after record, but there must be more to the technology than just crypto, or not? Well, we can take a look at the underlying technology first to understand what it actually provides to us.

\U0001f9f5\u2b07\ufe0f
— Oliver Jumpertz (@oliverjumpertz) February 16, 2021

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)

More from Machine learning

Abhishek Thakur
@abhi1thakur

In this thread, I will tell you how to learn python for data science in 1 hour 👇 1/N

It's not possible 2/N

It's not possible 3/N

It's not possible 4/N

It's not possible 5/N

Santiago
@svpino

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

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Pratham Prasoon
@PrasoonPratham

Everything you need to know about the math for machine learning as a beginner.

🧵👇

Before diving into the math, I suggest first having solid programming skills.

For example👇

(2 / 17)

In Python, these are the concepts which you must know:

- Object oriented programming in Python : Classes, Objects, Methods
- List slicing
- String formatting
- Dictionaries & Tuples
- Basic terminal commands
- Exception handling

(3 / 17)

If you want to learn python, these courses are freecodecamp could be of help to you.

🔗Basics: youtube .com/watch?v=rfscVS0vtbw
🔗Intermediate :youtube .com/watch?v=HGOBQPFzWKo

(4 / 17)

You need to have really strong fundamentals in programming, because machine learning involves a lot of it.

It is 100% compulsory.

(5 / 17)

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.

👇

$Unknown \U0001f464$

Unknown 👤
@console_404

10 PYTHON 🐍 libraries for machine learning.

Retweets are appreciated.
[ Thread ]

1. NumPy (Numerical Python)

- The most powerful feature of NumPy is the n-dimensional array.

- It contains basic linear algebra functions, Fourier transforms, and tools for integration with other low-level languages.

Ref: https://t.co/XY13ILXwSN

2. SciPy (Scientific Python)

- SciPy is built on NumPy.

- It is one of the most useful libraries for a variety of high-level science and engineering modules like discrete Fourier transform, Linear Algebra, Optimization, and Sparse matrices.

Ref: https://t.co/ALTFqM2VUo

3. Matplotlib

- Matplotlib is a comprehensive library for creating static, animated, and interactive visualizations in Python.

- You can also use Latex commands to add math to your plot.

- Matplotlib makes hard things possible.

Ref: https://t.co/zodOo2WzGx

4. Pandas

- Pandas is for structured data operations and manipulations.

- It is extensively used for data munging and preparation.

- Pandas were added relatively recently to Python and have been instrumental in boosting Python’s usage.

Ref: https://t.co/IFzikVHht4

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Nicolas Cole
@Nicolascole77

🚢 Atomic Essays Republishing Framework 🚢

1. Write Atomic Essay
2. Post image on Twitter
3. (Bonus) Copy/paste text as thread
4. Find relevant Question on Quora. Copy/paste Atomic Essay + image.
5. Copy/paste again on Medium.
6. Again on LinkedIn
7. Win

Guide below ✍️🚢👇

Step 1: Write Atomic Essay

I really enjoy writing right inside the Figma template. It helps give me a good sense of exactly how much "real estate" I have/have left before my time us up and I'm out of space.

Once finished, I export the image and send to my phone on Slack.

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These are usually power-phrases: things the reader skims and thinks, "That's interesting," prompting them to read more.

Step 3: For the Ship 30 for 30 challenge, I only turn Atomic Essays into threads if...

1. The piece is SUPER actionable and lends itself well to the thread format (lots of bullets, quick points, lists, etc.)

or...

2. I'm referencing research and want to link to the pieces.

Step 4: Find relevant Question on Quora. Copy/paste Atomic Essay + image.

Search on Quora around the topic you wrote about. Find a related Question. Copy/paste the whole Atomic Essay as your "answer" (& fix formatting). Then include image so it appears in the thumbnail.

$Brianne Kimmel \U0001f4ac$

Brianne Kimmel 💬...
@briannekimmel

Shocking fact: Millennial men are less likely to work than any other age and gender demographic in America.

Today, there are 500,000 young men missing from the U.S. workforce.

Research suggests video games & improved leisure tech plays a role in the problem. 👇 Thread:

Following the 2007 to 2009 recession, 25 to 34 year old men exited high school with fewer middle-skill job opportunities than years prior.

During this time, we saw an increased number of men living with parents & choosing unemployment over lower paying jobs.

It's estimated that 24M millennials live w/ their parents.

1 in 4 living in their parents’ home neither go to school nor work.

What's more surprising? 9 in 10 who lived with their parents a year ago are still living there w/ no plans to leave.

Economists are calling millennial men a lost generation.

According to economist David Dorn:

“If you get to the point where you’re turning 30, you’ve never held a real job and you don’t have a college education, then it is very hard to recover at that point.”

Economists suggest this choosiness is a generational trait.

Forbes interview w/ a high school educated man:

"I’m very quick to get frustrated when people refuse to pay me what I’m worth."
“People feel that they have choice nowadays, and they

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Anu Satheesh
@AnuSatheesh5

Alathiyur Hanuman Temple
Malappuram, Kerala 🚩
#keralatemples

One of the Powerful Anjaneya Temples in Kerala. Hanuman temple is adjacent to Shri Ramar Sannidhi. Here Hanuman's head is tilted towards the left to hear Shri Rama's words. Shri Rama is entrusting Hanuman👇

Task of locating Mata Sita in Lanka.

Prathishta of Hanuman is done by Sage Vasishta. Eventhough this is Shri Ram Temple, importance is given to Hanuman
Shri Rama shares with Hanuman the Abhignana Vaakhyam, the words that would enable him to gain the confidence of Mata Seetha.

Hanuman stands leaning a little towards the left, showing how he hear Shri Rama's words and also assuring him Your Wish is my Command.
The Vigraham of Hanuman gives us the Strength, Confidence and Bhakthi.
There is a place marked here to show Hanuman’s leap to Lanka over sea.

Devotees run and leap over the stone for better health.Aval Naivedhyam is famous here. Those children who have nightmares are told to pray Alathiyur Hanuman. Mantra 👇

Alathiyur Hanumane
Pedi Swampnam kanarudhe
Pedi Swapnam Kandalo
Val Kondu Thattiyunarthename
🙏🕉🙏

$Emperor\U0001f451$

Emperor👑
@EmperorBTC

Technical Analysis Masterclass- Part 2

'Entering the trade'

Entering a trade without ascertaining a certain things is gambling.

In this masterclass we will learn the pre-requisites to enter a trade.

DON'T ENTER A TRADE WITHOUT DETERMINNG THE FOLLOWING.

Please share.

We understand what reward to risk (popularly called risk to reward) is.
It will be denoted by R:R.

We will also try to bust a few myths about R:R and how to avoid losing trades.

Before entering a trade, you need to determine 3 things.

1. Entry trigger
2. Stop loss
3. Target

1. Entry trigger = Reasons for entering a trade. There could be multiple reasons or a single reason for entry.

Generally a set of reasons AKA confluence is a higher probability trade and a generally a safer entry.

Example of an entry trigger.

2. Stop Loss.

The price in the opposite direction of the trade where the trade is exited, at a loss.

At this level, the reason for the entry becomes invalidated according to TA and the price can then move in the opposite direction, probabilistically.

3. Target is the possible price level that the asset might touch based on previous trends or confluence AND where a possible reversal could occur.

Target is the next path of least resistance from where the price might reverse.

We will always ONLY use TA to determine all 3.

Robbie Andrew
@robbie_andrew

I doubt there are many outside of Norway that know that the Norwegian pop group A-ha was critical in the introduction of electric car incentives in Norway. I certainly didn't. Read on...

In 1989, two members of the group, Morten Harket and Magne Furuholmen were in Switzerland with environmentalist Frederic Hauge, when they came across a hobby-converted Fiat Panda. Stated range: 45 km. They snapped it up and imported it to Norway.

Photo: In Switzerland (Bellona)

On arrival in Norway, the regs didn't accommodate the registration of electric cars so it couldn't legally be driven on the road. Since it had a propane-fuelled heater, just like a motorhome, they registered it as a motorhome.

Moreover, in contrast to petrol cars, diesel cars paid registration fees based on how far they were driven, and our heroes thought this should also apply to electric cars. So the two-seater Swiss-converted Fiat Panda was registered as a diesel motorhome in Norway.

They managed (somehow) to avoid paying the one-off registration fee in 1990, and ever since then electric cars have been exempted this now-substantial fee.
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