This is how I got three job offers as a 16-year-old high school student.

( and why I rejected them )
🧵👇

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)

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)

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)

Here's everything you need to know about the math for machine learning.

(+ free resources )
🧵👇

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

For example👇

(2 / 18)

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 / 18)

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

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

(4 / 18)

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

It is 100% compulsory.

(5 / 18)

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)

If there is one thing I have learned it is that people love bullshit. They buy bullshit stories from professional bullshit artists, accept bullshit philosophies, purchase bullshit how-to-books, vote for bullshitting politicians.

At first, I resisted believing this because it is illogical and dumb and frankly, doesn't reflect well on the human race of which I am a member (and thus have long been hopeful that it is more of a distinction than it actually turns out to be.)

But if you actually are interested in facts you begin to notice how little most people care for them. Sometimes it is because they are heard to learn and bullshit is so much more widely and easily available. Sometimes it is because facts make them uncomfortable about themselves.

Sometimes it is because just one tiny fact will upset a whole worldview that has grown as comfortable as an overstuffed chair. (Settling into such worldviews is a lot like settling into chairs like that because the longer you're there and the deeper you sink...

...the harder it is to hoist yourself up and out of your fat-assed intellectual laziness.) But I actually think the reason people have such an appetite for bullshit goes deeper, goes to an aspect of ourselves we just can't accept.

Imagine going about your life, when a virus develops into a pandemic. Suddenly the rulers of your country are like "We're going to let the virus infect us, don't listen to the outside world".

Most people in your country are like "Awesome!", and ridicule you if trying to opt out

You hear about all these stuff like chronic health issues the virus causes, in different research papers available on the internet. It appears to be a serious problem, likely to ruin many lives. You do however never hear it mentioned in the debate in your country.

On the news you hear people with fancy titles, downplaying the virus, and ridiculing other countries for trying to stop it.

The resistance is futile, they say. We are all going to get infected in the end anyways.

You see the schools in other countries closing down, but in your own country they remain open. Parents are not allowed to keep their children at home. If they do, they are punished by the government, shunned as bad parents by those who think the herd immunity strategy is awesome.

You later find out that the people leading the response to the pandemic, reasons around how it is beneficial to keep schools open in e-mails with each other. They reflect on the benefits of open schools, for children getting infected, thus contributing to the herd immunity.

Some of you will probably know I'm currently grieving. It's a month since my fantastic sister died. I've been thinking a lot about loss, progress and reckoning and coming to terms, both personally and as a society. And I've been wondering: how will we remember the pandemic dead?

The UK is filled with memorials to the dead of wars, of disasters, of lifeboat crews and atrocities. We are a landscape haunted by attempts to remember the brave and the innocent. Villages, towns, businesses, communities: all grouped together to pay for plaques, statues, gardens

I have a terror that in our desire to get 'back to normal' we will forget those lost and things lost during the current pandemic. There's not an enemy they were overcome by; not a single flashpoint of tragedy. Those lost to the pandemic are an uncomfortable reminder of failure

Rebecca Solnit talks about hope only coming from knowing history. People may be optimistic about the future post-covid-19, but that's just desire and dream. For us to build a hopeful future we need to acknowledge and feel what has been lost; to repair and grow, not just erase

The thing is, I don't know if our communities know how to remember what is lost, not in ways that are meaningful to all of that community. Religion is a great enabler of remembrance, but that's not something everyone has. I'm wondering how we fully inhabit our collective sorrow

Instead of today's thread, let's have some fun

1 RT = 1 Life Lesson

First one is on the house.

-Thread-

Those you trust the most are those in the best position for betrayal.

Active listening is a false God.

Most people will waste your mental space.

If you want to say "no", dont use any other word.

Seeking approval is the first step to auto destruction