BC MACHINE LEARNING
Saved by @Alex1Powell

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)
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 👇

MacOS: 🔗docs.​anaconda.​com/anaconda/install/mac-os/
Windows: 🔗docs.​anaconda.​com/anaconda/install/windows/
Linux: 🔗docs.​anaconda.​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'a a guide with step by step instructions which will help you.
🔗bit.​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.​e has loads of features.

(11 / 19)
If all of that seems complicated to you, I suggest you use Google colab, Kagggle notebooks or repl.​it
These are online editors which have everything setup 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 : 🔗colab.​research.​google.​com
Kaggle Notebooks : 🔗kaggle.​com/notebooks/welcome
Repl : 🔗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: 🔗youtube.​com/watch?v=rfscVS0vtbw
Intermediate: 🔗youtube.​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.

🔗bit.​​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 👇

​🔗youtube.​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: 🔗youtube.​com/watch?v=HDmwiJxzIrw
Mac: 🔗youtube.​com/watch?v=3TAEC-1YUZw
Linux: 🔗youtube.​com/watch?v=HbgzrKJvDRw

(18 / 19)

More from Pratham Prasoon

Pratham Prasoon
Pratham Prasoon
@PrasoonPratham
Can you get a job in data science and machine learning without a college degree?

🧵👇

Short Answer: Yes.

Long Answer, keep reading 👇

(Advice from industry experts who I talked to.)

Companies are looking for people who add value.
In order to add value, you'll need skills. Simple as that.

Get the skills to provide value and you'll get the job.

In the age of the internet where everything is pretty much free, why do college degrees matter?

A college degree makes it easier to get the skills to get a job in machine learning or data science.

College degrees also help you make many useful connections and provides many opportunities in the form of internships and whatnot.

College degrees have their own place
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
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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
Pratham Prasoon
Pratham Prasoon
@PrasoonPratham
This is how I got three job offers as a 16-year-old high school student.

( and why I rejected them )
🧵👇

June 2020.

I receive a DM from a college student in California. The message read something like this "Hey Pratham, I am one of the co-founders of XYZ Inc, a robotics company, we are looking to add enthusiastic people to our team, would you be interested?"

(2 / 11)

It was the first time I had ever been offered a job, so I said Yes! (I did reject it later)

There were a couple of interview rounds I had to go through. None of them included technical questions, which was interesting.

(3 / 11)

After the first interview, I saw the list of the roles I could apply for. I realized how little I knew, terms like "Ui/UX, ROS(Robot operating system)", at that time, I was overwhelmed.

(4 / 11)

I knew that this wasn't a job for me. I decided that I would reject this job role even if I cleared the second round, which I ended up doing.

(5 / 11)
LIFE
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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Roger Grosse
Roger Grosse
@RogerGrosse
Important paper from Google on large batch optimization. They do impressively careful experiments measuring # iterations needed to achieve target validation error at various batch sizes. The main "surprise" is the lack of surprises. [thread]

https://t.co/7QIx5CFdfJ


The paper is a good example of lots of elements of good experimental design. They validate their metric by showing lots of variants give consistent results. They tune hyperparamters separately for each condition, check that optimum isn't at the endpoints, and measure sensitivity.

They have separate experiments where the hold fixed # iterations and # epochs, which (as they explain) measure very different things. They avoid confounds, such as batch norm's artificial dependence between batch size and regularization strength.

When the experiments are done carefully enough, the results are remarkably consistent between different datasets and architectures. Qualitatively, MNIST behaves just like ImageNet.

Importantly, they don't find any evidence for a "sharp/flat optima" effect whereby better optimization leads to worse final results. They have a good discussion of experimental artifacts/confounds in past papers where such effects were reported.
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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Abhishek Thakur
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
MACHINE LEARNING
John Burn-Murdoch
John Burn-Murdoch
@jburnmurdoch
Really enjoyed digging into recent innovations in the football analytics industry.

>10 hours of interviews for this w/ a dozen or so of top firms in the game. Really grateful to everyone who gave up time & insights, even those that didnt make final cut 🙇‍♂️ https://t.co/9YOSrl8TdN


For avoidance of doubt, leading tracking analytics firms are now well beyond voronoi diagrams, using more granular measures to assess control and value of space.

This @JaviOnData & @LukeBornn paper from 2018 referenced in the piece demonstrates one method https://t.co/Hx8XTUMpJ5


Bit of this that I nerded out on the most is "ghosting" — technique used by @counterattack9 & co @stats_insights, among others.

Deep learning models predict how specific players — operating w/in specific setups — will move & execute actions. A paper here: https://t.co/9qrKvJ70EN


So many use-cases:
1/ Quickly & automatically spot situations where opponent's defence is abnormally vulnerable. Drill those to death in training.
2/ Swap target player B in for current player A, and simulate. How does target player strengthen/weaken team? In specific situations?
MACHINE LEARNING
Santiago
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
MACHINE LEARNING

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Here's the most useful #Factualist comparison pages #Thread 🧵


What is the difference between “pseudonym” and “stage name?”

Pseudonym means “a fictitious name (more literally, a false name), as those used by writers and movie stars,” while stage name is “the pseudonym of an entertainer.”

https://t.co/hT5XPkTepy #english #wiki #wikidiff

People also found this comparison helpful:

Alias #versus Stage Name: What’s the difference?

Alias means “another name; an assumed name,” while stage name means “the pseudonym of an entertainer.”

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A #story from #Thiruvilayadal puranam-one of divine leelas of Bhagwan Shiva

#threadseries #Madurai

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Once there was a great deluge,everything in the world was destroyed.All lifeforms that existed got perished.Afterwards ,due to Bhagwan Shiva creation started.Then from Bhagwan Shiv ji's mouth-birth of 🕉 OM- Pranavam happened.From Pranavam,4 vedas appeared. Rishis learned them.

Many Rishis in Naimisharanya learned Vedas&recited them but they didn't understand the meaning&essence of Vedas. They were worried as they couldn't find any guru who can teach them the meaning of Vedas.That time,Rishi Arabhatar visited them.Rishis told him about need their worry.

Rishi Arabhatar told them that Bhagwan Shiva has given Vedas &he would be grateful enough to teach the meaning as well.But for that rishis have to rigorous penance at a sacred place.Also told them to go to Sundareswarar temple in Madurai to do tapas in front of Dakshinamurthy.

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