BC MACHINE LEARNING
Saved by @Alex1Powell

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.

(4 / 15)
Here's the code👇

(5 / 15)
In order to avoid the hassle of the setup for python, we'll use collab which has all the libraries we need pre installed, a free GPU for faster learning and everything runs in the cloud!

🔗//colab.research.google.com/drive/1FxSrY6hwdgszzNydyobTLsMyO0bfpiPs?usp=sharing

(6 / 15)
Let's try to understand what is going on here

1. We import TensorFlow and Keras which are frameworks for making neural nets

2. Our Neural Net: This is where all the magic happens, for this exercise we need only one neuron.

Wait! What is a neural net?👇

(7 / 15)
Neural Networks are a digital imitation of the neurons you see in the human brain.

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

(8 / 15)
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.

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

(9 / 15)
It is almost impossible for a human to tune thousands of dials like these, but a computer certainly can.

Once the dials are well tuned, you have a well trained neural network!
...

(10 / 15)
... In this case we'll be able to predict the prices of houses based on how many flats they have.

3. Now we pass the data (flats and prices) through our neural network 500 times. (these loops are called epochs)

(11 / 15)
4. Finally,we predict what the price of a house with 10 flats. (we should get something around 100,000)

And that's it! It was that easy.
In case you want to improve your python and machine learning skills after this exercise, these are great frfee courses to take 👇

(12 / 15)
Machine learning foundations course
🔗//youtube.com/watch?v=_Z9TRANg4c0

> A simple yet extremely effective course on getting started with machine learning without all the crazy math.

(13 / 15)
The Basic & Intermediate Python course on freecodecamp
🔗Basics //youtube.com/watch?v=rfscVS0vtbw
🔗Intermediate //youtube.com/watch?v=HGOBQPFzWKo

(14 / 15)

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 Prasoon
Pratham Prasoon
@PrasoonPratham
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)
MACHINE LEARNING , PYTHONML
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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
Pratham
@PrasoonPratham
There are a million machine learning tutorials and courses out there which makes it really hard to choose the right one for you.

Here are some that I really like that you might find useful.
🧵 👇🏻

Before we begin, here's some unsolicited advice.

It is not about how many courses you take that matters but the problems that you solve with your knowledge.

You can take any combination of courses and tutorials from this list that you think will be useful.

Python crash course by Traversy Media (YouTube)

• Introduces you to a lot of basic concepts in Python
• Only 1.5 hours long


Intermediate Python course (YouTube)

• Helps you understand some of the more difficult concepts in Python


The neural networks series by 3blue1brown (YouTube)

• Visually explains neural networks
• Short and concise
ALL
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

More from 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
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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Cory Doctorow #BLM
Cory Doctorow #BLM
@doctorow
We hear a lot about Facebook as a platform for manipulation - using machine learning to bypass our critical faculties and trick us into believing things that are bad for us - but the real show is in Facebook's ability to target, not manipulate.

1/


People who hold disfavored views struggle to find one another and mobilize. To find other people that feel the same way as you and make common cause with them to effect political change, you have to reveal your views and suffer social sanction.

2/

Search allows people who hold these views to find one another. If you have a deep feeling about your gender being nonbinary but don't know the words for it, you can search for communities of people who have those words, join them, and discover who you've been all along.

3/

This is why, in this moment, so many ideas are migrating from the fringe to the center: ideas about racial justice, gender identity, alternatives to market systems, etc. People have harbored these views all along, but have held back on expressing them.

4/

Being able to express yourself in private, among people who share your views, is a prelude to going public and putting your case to the wider world in hopes of effecting change.

5/
MACHINE LEARNING
Jan Giacomelli
Jan Giacomelli
@jangiacomelli
🤔 Python decorators

What are they? How do you use them?

🧵 Let's find out 👇

1️⃣ Decorator is a function or a class that wraps another function or class modifying its behavior.

So how does that work?

The first thing to know is that everything in Python is an object - functions too


2️⃣ That means they can be passed to another function as an argument or returned from a function

Functions that take other functions as an argument are called higher-order functions


3️⃣ In Python, you can define a function inside other function - such functions are called inner functions


4️⃣ To create a decorator you just need to apply all of that together

log_enter_leave is a decorator.

my_function is the function.

To alter my_function's behavior we reassign it applying log_enter_leave decorator.
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

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2/ Usually at the 20 minute mark (sometimes I set a timer) I'm so engrossed in what I'm doing that I don't want want to stop and end up working on it for the next hour or two. But getting started seems to be the hardest part.

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Here is a special thread introducing our series on “The Making of the Islamic World.” It offers 10 episodes about the transformation of Islamicate societies between the 7th and 17th centuries. You can find the series page here: https://t.co/7mL9eWmMmz


This is Chris Gratien posting. I created this series during Fall 2020 for my students as asynchronous material supported by readings and discussion. This is going to be a long thread, but it will be a great shortcut for those looking to make similar use of these materials.

There are two items worth pointing out: 1. This is an overview of the political, social, and cultural history of Islamicate societies. It does not deal with the origins of Islam or many theological questions. It does discuss Islam’s spread and the transformation of Islamic law.

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1/ Stolen from the greatawakening(DOT)win...

I will only be making one post here. Do not ask who I am. Do with this information as you please.

The Trump Card is being played. It's much simpler than you all realize. Some of you have gotten it right.
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Margatha Natarajar murthi - Uthirakosamangai temple near Ramanathapuram,TN
#ArudraDarisanam
Unique Natarajar made of emerlad is abt 6 feet tall.
It is always covered with sandal paste.Only on Thriuvadhirai Star in month Margazhi-Nataraja can be worshipped without sandal paste.


After removing the sandal paste,day long rituals & various abhishekam will be https://t.co/e1Ye8DrNWb day Maragatha Nataraja sannandhi will be closed after anointing the murthi with fresh sandal paste.Maragatha Natarajar is covered with sandal paste throughout the year


as Emerald has scientific property of its molecules getting disturbed when exposed to light/water/sound.This is an ancient Shiva temple considered to be 3000 years old -believed to be where Bhagwan Shiva gave Veda gyaana to Parvati Devi.This temple has some stunning sculptures.
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Anshul Pandey
Anshul Pandey
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IMPORTANCE, ADVANTAGES AND CHARACTERISTICS OF BHAGWAT PURAN

It was Ved Vyas who edited the eighteen thousand shlokas of Bhagwat. This book destroys all your sins. It has twelve parts which are like kalpvraksh.

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and characters of Pandavas are described by the dialogues between Suutji and Shaunakji. Then there is the story of Parikshit.
Next there is a Brahm Narad dialogue describing the avtaar of Bhagwan. Then the characteristics of Puraan are mentioned.

It also discusses the evolution of universe.( https://t.co/2aK1AZSC79 )

Next is the portrayal of Vidur and his dialogue with Maitreyji. Then there is a mention of Creation of universe by Brahma and the preachings of Sankhya by Kapil Muni.


In the next section we find the portrayal of Sati, Dhruv, Pruthu, and the story of ancient King, Bahirshi.
In the next section we find the character of King Priyavrat and his sons, different types of loks in this universe, and description of Narak. ( https://t.co/gmDTkLktKS )


In the sixth part we find the portrayal of Ajaamil ( https://t.co/LdVSSNspa2 ), Daksh and the birth of Marudgans( https://t.co/tecNidVckj )

In the seventh section we find the story of Prahlad and the description of Varnashram dharma. This section is based on karma vaasna.
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