BC MACHINE LEARNING
Saved by @CodyyyGardner

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)
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
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
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
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Pratham Prasoon
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)
MACHINE LEARNING
Pratham Prasoon
Pratham Prasoon
@PrasoonPratham
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)
MACHINE LEARNING
Pratham
Pratham
@PrasoonPratham
A git cheat sheet for all your needs!

https://t.co/H7QdOrthwV
GIT

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Santiago
Santiago
@svpino
For a long time, I didn't understand how to use Virtual Environments in Python 🐍.

If this is just, let's end it here and now: 🧵👇

[2] Virtual Environments let you deal with the dependencies that your code has with external Python libraries.

It avoids having conflicts when your projects depend on different versions of the same library.

👇

[3] Let's imagine that you are building your first Python project and you install the "requests" library:

pip install requests

You get version 2.24.0 installed in your system.

👇

[4] A month later, you decide to work on your second project. It also needs the "requests" library.

But the latest version is not 2.24.0 anymore.

Now version 3 is available, and that's the one you want to use!

👇

[5] You could upgrade your entire system to version 3, but then you'll be potentially breaking the first project you built that depends on 2.24.0!

Can you imagine this happening on a server with many more applications running?

👇
MACHINE LEARNING
Cassie Kozyrkov
Cassie Kozyrkov
@quaesita
1/🧵 Good #DataScience advice that breaks pretty much every rule you learned in class... a thread. (+full blog post linked)

English version: https://t.co/dG4l6vPFBT
Spanish version: https://t.co/gFAjPQ5clS

#AI #MachineLearning #Statistics #RStats

2/🧵 Allow your approach to be sloppy at first and burn some of your initial time, energy, and data on informing a good direction later. That's right, you're supposed to start sloppily ON PURPOSE.

3/🧵 Have a phase where the only result you’re after is *an idea of how to design your ultimate approach better.*

4/🧵 In other words, start with a pilot phase where the objective isn't finding answers, it's finding a good approach to finding answers.

5/🧵 That means you're encouraged (ENCOURAGED!) to start with everything your stats classes told you not to do:
MACHINE LEARNING
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Marcus Mengs
Marcus Mengs
@mame82
Okay, doing my first baby steps with r2frida (which combines the power of @radareorg and @fridadotre).

Gonna share my progress in this thread (live, so keep calm).

The goal: Runtime inspection of data sent out by TikTok !!before!! it gets encrypted

1/many

First of all, we do not start from zero. I got some prior knowledge from past reversing attempts and want to share some important facts.

TikTok's (log data) encryption is accomplished by a native library. The Android Java code just serves as proxy function to the native function

The decompiled code for the respective native JNI function of an older TikTok version looks something like this, but in this example I use the most current TT version (no statical analysis done, yet)


In case you never reversed native libraries which were build to interface with Android Java layer via JNI, I highly suggest the entry level introduction on the topic by @maddiestone

Before we start, I want to pinpoint some important aspects (which are also covered by Maddie's videos).

1) Unlike raw C-functions, JNI functions like the one showcased above, receive pointers to complex Java objects .

F.e. a function receiving a String on the Java layer...
MACHINE LEARNING
Quant@Python
Quant@Python
@BeingHorizontal
Thread for beginners; How to learn machine learning.

Let’s start with something really simple and useful not just for Machine Learning.

Start with a Python library called pandas. It’s a huge library but it's mostly fun for traders because it's kind of rewarding to learn each function and how it gonna affect the way you handle the financial data.

Then learn some basic stuff about Python library called NumPy. Pandas is built on NumPy. Numpy is huge, you just need to understand some basic NumPy functions like NumPy shapes and NumPy arrays

If you understand pandas then you will understand NumPy arrays and shapes easily. Side note: Numpy shapes gonna play an important part in the deep learning model called LSTM - mainly used for time-series analysis(such as financial data) and Natural Language Processing (NLP)
MACHINE LEARNING
Pratham Prasoon
Pratham Prasoon
@PrasoonPratham
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)
MACHINE LEARNING

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MINUTE DETAILS ABOUT THE ATTACK ON SHAISTA KHAN AT LAL MAHAL

Remarkable Commando op

A long 🧵 but worth your time

Aurangzeb sent his maternal uncle Shaista Khan with powerful army over 1,50,000 on the request of Badibegum Sahiba, Adilshahi sultanate

1/


Shaista Khan entered Deccan to kill Chhatrapati Shivaji Maharaj & to destroy his empire. Shahista Khan captured a lot of province and fort of Chakan near Pune.

2/


Till 3 years he gave lot of troubles to the subjects of Chhatrapati Shivaji Maharaj and he personally made Lal Mahal his destination to live at , which was the property of Chhatrapati Shivaji Maharaj . Lal Mahal, where Chhatrapati Shivaji Maharaj spent his childhood.

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1. In the April 1663 Chhatrapati Shivaji Maharaj personally made a surprise attack on Shahista Khan in the Lal Mahal Pune.

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1

From today, we will memorize the names of 27 Nakshatras in Vedic Jyotish to never forget in life.

I will write 4 names. Repeat them in SAME sequence twice in morning, noon, evening. Each day, revise new names + recall all previously learnt names.

Pls RT if you are in.

2

Today's Nakshatras are:-

1. Ashwini - अश्विनी

2. Bharani - भरणी

3. Krittika - कृत्तिका

4. Rohini - रोहिणी

Ashwini - अश्विनी is the FIRST Nakshatra.

Repeat these names TWICE now, tomorrow morning, noon and evening. Like this tweet if you have revised 8 times as told.

3

Today's Nakshatras are:-

5. Mrigashira - मृगशिरा

6. Ardra - आर्द्रा

7. Punarvasu - पुनर्वसु

8. Pushya - पुष्य

First recall previously learnt Nakshatras twice. Then recite these TWICE now, tomorrow morning, noon & evening in SAME order. Like this tweet only after doing so.

4

Today's Nakshatras are:-

9. Ashlesha - अश्लेषा

10. Magha - मघा

11. Purvaphalguni - पूर्वाफाल्गुनी

12. Uttaraphalguni - उत्तराफाल्गुनी

Purva means that comes before (P se Purva, P se pehele), and Uttara comes later.

Read next tweet too.

5

Purva, Uttara prefixes come in other Nakshatras too. Purva= pehele wala. Remember.

First recall previously learnt 8 Nakshatras twice. Then recite those in Tweet #4 TWICE now, tomorrow morning, noon & evening in SAME order. Like this tweet if you have read Tweets #4 & 5, both.
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In Academia, people aren’t always supported when they need it. I want to share what @WesternU did when my husband died right after the pandemic shutdown. It should be shared with other uni’s as a model for empathy and proactive care when someone is in crisis.
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