BC MACHINE LEARNING
Saved by @Alex1Powell

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.

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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.

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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.

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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.

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Google Collab

Collab is a jupyter notebook running on google's servers which gives you access to GPUs and TPUs for training machine learning models faster for free, yes free.

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Kaggle

I like to call Kaggle the codepen for machine learning and data science.This is the place where you show off you machine learning skills. You have access to datasets for which you can make machine learning models and compete with other people around the world.

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TensorFlow

TensorFlow is a framework for machine learning,it has variants like TensorFlow.js for machine learning in the browser, TensorFlow lite for machine learning on mobile phones, and the standard TensorFlow library.

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PyTorch

PyTorch is an open-source machine learning library based on the Torch library,used for applications such as computer vision and natural language processing. It is very similar to TensorFlow in the things you can do in it with differences in the syntax.

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Matplotlib

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

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NumPy

Numpy replaces the lists in Python with its lists, but why? Aren't the default lists good enough? The thing is that NumPy lists are much faster than Python lists, hence the wide usage of NumPy.

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SciKit Learn

SciKit learn is a machine learning library that features various classification, regression, and clustering algorithms including support vector machines. These are complex computations you may need in training your machine learning model.

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This thread took over 3 hours to make, your support by following me if you like this content will be highly appreciated! 🙏🔥

Stay tuned for more threads, good luck in your machine learning journey.

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More from Pratham Prasoon

Pratham
Pratham
@PrasoonPratham
A git cheat sheet for all your needs!

https://t.co/H7QdOrthwV
GIT
Pratham Prasoon
Pratham Prasoon
@PrasoonPratham
Machine learning terms you must know about as a beginner.

🧵👇

These terms won't mean anything unless you know what Machine learning is all about.

> Machine learning is the process of making a program which allows a computer to learn from data.

The data could be anything, images, audio or even text.

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In machine learning we use something called a neural network, this is essentially an imitation of the human brain.

> Neural Networks are a digital imitation of the neurons you see in the human brain.

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In these neural networks, data flows through them and each neuron (the circle) has a numerical value which will change.

> The value of a neuron gets changes to something which is close to what we want each time the data passes through the neural network.

(4 / 11)


Think of the neurons as dials on a lock, you have to tune every dial to open the lock.

It is almost impossible for a human to tune thousands of dials like these, but a computer certainly can.

(5 / 11)
MACHINE LEARNING
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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👇

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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 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
Pratham Prasoon
Pratham Prasoon
@PrasoonPratham
A simple guide on how to train your first neural network in under 10 minutes to kick-start your machine learning journey.

(+ no setup required and free extra resources)
🧵👇

For this exercise, you'll need :
- A computer/phone
- An internet connection
- Very basic Python knowledge
- Willingness to learn

What you do not need:
- Complex math
- An expensive computer
- A PhD

Math will come later on in your machine learning journey :)

(2 / 15)

Here's our problem,we are given data in which we are given the number of flats in a house and its corresponding price. Like a house with one flat is worth 10000, and 20000 for a house with 2 flats.

(3 / 15)


We can clearly tell that the price of the house increases by 10000 per extra flat however our computer does not know this and we won't it tell it about this, it'll have to figure things out on its own.

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Here's the code👇

(5 / 15)
MACHINE LEARNING

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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
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)
MACHINE LEARNING
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Fionna O'Leary, \U0001f56f\U0001f1ea\U0001f1fa
Fionna O'Leary, 🕯🇪🇺...
@fascinatorfun
Thanks for this incredibly helpful analysis @dgurdasani1

Two questions. 1/ Does this summarise the AZ published data :
The plan is to extend the time interval for all age groups despite it being largely untested on the over 55yrs, although the full data is not yet published


Do we have the actual numbers of over 55yr olds given a 2nd dose at c12 weeks and the accompanying efficacy data?

Not to mention the efficacy data of the full first dose over that same period?

I’d quite like to know whether I am to be a guinea pig & the ongoing risks to manage

You attached photos of excerpts from a paper. Could you attach the link?

Re Pfizer. As I understand it the most efficacious interval for dosing was investigated at the start of the trial.


Here’s the link to the

I’ve got to say that this way of making and announcing decisions is not inspiring confidence in me and I am very pro vaccination as a matter of principle, not least because my brother caught polio before vaccinations available.
MACHINE LEARNING
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
Santiago
Santiago
@svpino
An introduction to one of the the most basic structures used in machine learning: a tensor.

🧵👇

Tensors are the data structure used by machine learning systems, and getting to know them is an essential skill you should build early on.

A tensor is a container for numerical data. It is the way we store the information that we'll use within our system.

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Three primary attributes define a tensor:

▫️ Its rank
▫️ Its shape
▫️ Its data type

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The rank of a tensor refers to the tensor's number of axes.

Examples:

▫️ The rank of a matrix is 2 because it has two axes.
▫️ The rank of a vector is 1 because it has a single axis.

(4 / 16)

The shape of a tensor describes the number of dimensions along each axis.

Example:

▫️ A square matrix may have (3, 3) dimensions.
▫️ A tensor of rank 3 may have (2, 5, 7) dimensions.

(5 / 16)
MACHINE LEARNING , TENSOR BASICS

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Let folks have their many talents, interests and gifts. Life is far more fun with variety, loves.

A lot of folks have come to know me as an activist & I’m grateful that folks care to know me at all.

But I wasn’t born in 2014. I was a whole teacher, executive, policy person, speaker, arts and culture lover, reader, writer, woman of faith, fashion and more before 4 yrs ago 🤷🏾‍♀️

We rightfully complain that marginalized people are not allowed to be fully human.

But we internalize and transfer our oppression daily. It’s a smog. We all breathe it in & act it out.

And then tell WoC “girl ain’t you supposed to be a _______? Why you doing ____?”

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And don’t go reading anything personal into this-this isn’t about me necessarily and it’s no subtweet (I try hard not to do that.)

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👇

Better: use one and make a MVP during the #24hrstartup challenge!

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Observing the public conversation around FB, and the private ones happening among techies and ex-FBers, I think the mutual misunderstanding is worse than when I set out two years (and 500 pages) ago to (in a small way) bridge that gulf.

We're basically fucked.

The tech world has gotten so huge, self-reinforcing, and insulated from reality they can no longer even vaguely look at themselves (and their actions) as others do. They just live on a different planet than most people.

Conversely, the average tech consumer doesn't understand the technology that has slowly taken over their lives, and their designated emissaries to figure it out--politicians, pundits, regulators, journalists--understand it barely better than they do, and have their own agendas.

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Techies take weird, improbable visions, and make them realities: some BS pitch deck to a VC, mixed with money and people, really does turn into some novel thing.

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