BC MACHINE LEARNING
Saved by @Alex1Powell

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)
👉Another question that I get asked quite often is when should you even start learning the math for machine learning?

(6 / 18)
👉Math for machine learning should come after you have worked on a project or two, doesn't have to a complex one at all, but one that gives you a taste of how machine learning works.

(7 / 18)
👉Here's how I do it, I look at the math when I have a need for it.

For instance I was recently competing in a kaggle machine learning challenge.

(8 / 18)
I was brainstorming about which activation function to use in a part of my neural net, I looked up the math behind each activation function and this helped me to choose the right one.

(9 / 18)
The topics of math you'll have to focus on for machine learning
- Linear Algebra
- Calculus
- Trigonometry
- Algebra
- Statistics
- Probability

Now here are the resources and a brief description about them.

(10 / 18)
Neural Networks
> A series of videos that go over how neural networks work with approach visual, must watch.

🔗youtube. com/watch?v=aircAruvnKk&list=PLZHQObOWTQDNU6R1_67000Dx_ZCJB-3pi

(11 / 18)
Seeing Theory
> This website helps you learn statistics and probability in an intuitive way.

🔗seeing-theory. brown. edu/basic-probability/index.html

(12 / 18)
Gilbert Strang's lectures on Linear Algebra (MIT)

> This is 15 years old but still 100% relevant today!
Despite the fact these lectures are made for freshman college students at MIT,I found it very easy to follow👌

🔗youtube. com/playlist?list=PL49CF3715CB9EF31D

(13 / 18)
Essence of Linear Algebra
> A beautiful playlist of videos which teach you linear algebra through visualisations in an easy to digest manner

🔗youtube. com/watch?v=fNk_zzaMoSs&list=PLZHQObOWTQDPD3MizzM2xVFitgF8hE_ab

(14 / 18)
Khan Academy
>The resource you must refer to when you forget something or want to revise a topic super quick

🔗khanacademy. org/math

(15 / 18)
Essence of calculus
> A beautiful series on calculus, makes everything seem super simple.

🔗youtube. com/watch?v=WUvTyaaNkzM&list=PL0-GT3co4r2wlh6UHTUeQsrf3mlS2lk6x

(16 / 18)
The math for Machine learning e-book

> This book is for someone who knows quite a decent amount of high school math like trignometry, calculus, I suggest reading this after having the fundamentals down on khan academy.

mml-book. github .io

(17 / 18)
If you found this thread helpful then don't forget to follow me, it takes a ton of effort to write these threads and your support keeps me going. 🔥🙏

Good luck in your machine learning journey!

(18 / 18 🎉)

More from Pratham Prasoon

Pratham Prasoon
Pratham Prasoon
@PrasoonPratham
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)
MACHINE LEARNING , PYTHON USEFUL
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
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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 what you'll hear when you first get into machine learning.

- Neural networks
- Loss
- Weights
- Biases
- Epochs
- Neurons
- Optimizers
...

It can get very confusing really fast!

Here are some of the terms you should know about.
(I wish I had this before)

🧵👇


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.

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.


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.


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.
MACHINE LEARNING
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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The "polarization is the problem" narrative to me is bad because it implies the status quo was a stable and acceptable set of trade-offs. It's the equivalent of a pollyanna "why can't we all get along" but the we in question is white supremacy and its targets.

like i understand that conflict mediation requires mutual concessions (i did my MA in conflict studies) and while it's important to meet people's deep needs across spectrums of difference, certain things are just not up for negotiation.

Also while peacebuilding work is good. It's not just "seeing each other as human" it's literally about transforming structural issues. (hence Galtung's distinction between positive and negative peace wherin negative peace is like a ceasefire and positive is a transformed society.

And I think that to whatever extent that kind of peacebuilding work is applicable, it's not between fascists and people who think fascists are bad. Fascists are not invited. They are only invited if they seize territories and can't be crushed. Then secret negotiations.

The US is always like "we don't negotiate with terrorists" but literally every president negotiates with who they consider to be terrorists. It's all just down low. But that's just because counter-insurgency failed.
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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
Advertisement
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
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
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Santiago
Santiago
@svpino
11 key concepts of Machine Learning.

— Supervised Learning Edition —

🧵👇

😜

Before starting, remember that, if you follow me, one of your enemies will be immediately destroyed (and you'll get to read more of these threads, of course.)

And if you don't follow me, well, you just hurt my feelings.

😜

1. Labels

(Also referred to as "y")

The label is the piece of information that we are predicting.

For example:

- the animal that's shown in a picture
- the price of a house
- whether a message is spam or not

👇

2. Features

(Also referred to as "x")

These are the input variables to our problem. We use these features to predict the "label."

For example:

- pixels of a picture
- number of bedrooms of a house
- square footage of a house

👇

3. Samples

(This is also known as "examples.")

A sample is a particular instance of data (features or "x.") It could be "labeled" or "unlabeled."

👇
MACHINE LEARNING

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राम-रावण युद्ध समाप्त हो चुका था। जगत को त्रास देने वाला रावण अपने कुटुम्ब सहित नष्ट हो चुका था।श्रीराम का राज्याभिषेक हुआ और अयोध्या नरेश श्री राम के नेतृत्व में चारों दिशाओं में शन्ति थी।
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माता सीता भी हनुमान को पुत्रवत मानती थी। अत: हनुमान अयोध्या में ही रह गए ।राम दिनभर दरबार में, शासन व्यवस्था में व्यस्त रहते थे। संध्या को जब शासकीय कार्यों में छूट मिलती तो गुरु और माताओं का कुशल-मंगल पूछ अपने कक्ष में जाते थे। परंतु हनुमान जी हमेशा उनके पीछे-पीछे ही रहते थे ।


उनकी उपस्थिति में ही सारा परिवार बहुत देर तक जी भर बातें करता ।फिर भरत को ध्यान आया कि भैया-भाभी को भी एकांत मिलना चाहिए ।उर्मिला को देख भी उनके मन में हूक उठती थी कि इस पतिव्रता को भी अपने पति का सानिध्य चाहिए ।

एक दिन भरत ने हनुमान जी से कहा,"हे पवनपुत्र! सीता भाभी को राम भैया के साथ एकांत में रहने का भी अधिकार प्राप्त है ।क्या आपको उनके माथे पर सिन्दूर नहीं दिखता?इसलिए संध्या पश्चात आप राम भैया को कृप्या अकेला छोड़ दिया करें "।
ये सुनकर हनुमान आश्चर्यचकित रह गए और सीता माता के पास गए ।


माता से हनुमान ने पूछा,"माता आप अपने माथे पर सिन्दूर क्यों लगाती हैं।" यह सुनकर सीता माता बोलीं,"स्त्री अपने माथे पर सिन्दूर लगाती है तो उसके पति की आयु में वृद्धि होती है और वह स्वस्थ रहते हैं "। फिर हनुमान जी प्रभु राम के पास गए ।
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12 Most Powerful Hanuman Mantras

Hanuman ji is the Hindu God of strength, valor, agility, and wisdom. He is considered as the incarnation (avatar) of bhagwan Shiva. Hanuman ji is the symbol of devotion and dedication. He is the greatest devotee of Prabhu Shri Ram. 1/n


Hanuman ji is known by several names such as Bajrangabali, Mahabali, Maruti Nandan, Pawanputra, Mahaveer, and Anjaneya. Hanuman ji is the son of Bhagwan Vayu, the wind god.

God Hanuman played a prominent part in rescuing Mata Sitafrom the bondage of demon King of Lanka, Ravana.


One can worship Hanuman ji by chanting Hanuman Mantras. There are many different types of Hanuman Mantras, each fulfilling a specific purpose. Hanuman Mantras help to beget the blessings of Mahabali Hanuman for success. 3/n


Let’s explore these powerful Hanuman Mantras one by one. Here’s the list for you:

1. Hanuman Moola Mantra
!! Om Hanumate Namah !!
ऊँ हनुमते नम:

You can chant the “Hanuman Moola Mantra if you usually face problems and obstacles in your life.4/n
#hanumanjanmotsav


However, the Human Moola Mantra is also a very powerful success mantra.

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celebrated during the Tamil month of Vaikasi (April–May), being the most prominent. The festival also symbolises Hindu-Muslim unity in the region – the flag of the chariot is provided by Muslims; they take offerings from the temple and present to Allah in the mosques.


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Understand that ethical wealth creation is possible. If you secretly despise wealth, it will elude you.

Ignore people playing status games. They gain status by attacking people playing wealth creation games.

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