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
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
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
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Pratham Prasoon
Pratham Prasoon
@PrasoonPratham
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)
MACHINE LEARNING
Pratham
Pratham
@PrasoonPratham
Everyone tells you how important math is in machine learning.

But I believe that if you want to learn machine learning today, there are things more important than math which very few people will tell you about.

Here are 5 of them.

🧵 👇🏻

The field of machine learning has been very academic which is why there is so much emphasis on learning math for it.

But, today with the frameworks that we have, not knowing how to multiply matrices is far less of an issue than knowing how to use the tools in this list 👇🏻

Yes, I know; you have R, Julia, and whatnot for machine learning but as of today, Python is the industry standard.

Get your data structures and algorithms right in Python because you will need them, here are some concepts worth learning 👇🏻


Let's say you're building the next big GPT-3 powered app that's going to take over the world.

You add a new feature to your app but it breaks the entire thing, what do you do now?

Learn Git, the ultimate version control tool.

I use this cheat sheet for all my git needs 👇🏻


You will be constantly be working on multiple packages which specific python version requirements 🐍

Virtual environments like Anaconda or Poetry will make this process much easier for you.

Who likes to see broken dependencies while running code?
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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

More from Machine learning

Alejandro Piad Morffis
Alejandro Piad Morffis
@AlejandroPiad
This is a Twitter series on #FoundationsOfML.

❓ Today, I want to start discussing the different types of Machine Learning flavors we can find.

This is a very high-level overview. In later threads, we'll dive deeper into each paradigm... 👇🧵

Last time we talked about how Machine Learning works.

Basically, it's about having some source of experience E for solving a given task T, that allows us to find a program P which is (hopefully) optimal w.r.t. some metric


According to the nature of that experience, we can define different formulations, or flavors, of the learning process.

A useful distinction is whether we have an explicit goal or desired output, which gives rise to the definitions of 1️⃣ Supervised and 2️⃣ Unsupervised Learning 👇

1️⃣ Supervised Learning

In this formulation, the experience E is a collection of input/output pairs, and the task T is defined as a function that produces the right output for any given input.

👉 The underlying assumption is that there is some correlation (or, in general, a computable relation) between the structure of an input and its corresponding output and that it is possible to infer that function or mapping from a sufficiently large number of examples.
MACHINE LEARNING
Pratham Prasoon
Pratham Prasoon
@PrasoonPratham
Are you planning to learning machine learning this year?

These are the Python frameworks you need to learn as an absolute beginner.

🧵👇

It can be overwhelming seeing all the machine learning frameworks at once and not even knowing what they do.

I made this thread so that your machine learning journey becomes just a little bit easier.

(2 / 11)

This thread will only focus on Python frameworks.

Why?
Because Python is the most common path into machine learning.

( If you have experience with any other language then learning Python will not be difficult at all. )

(3 / 11)

Here's the entire list:

- Pandas
- Numpy
- Matplotlib
- TensorFlow
- PyTorch
- Keras
- Scikit Learn

Let's see what they do.

(4 / 11)

You'll have to interact with csv files or databases on a regular basis to access data in machine learning, pandas helps you do just that.

The cool thing about Pandas is that it actually doesn't change the original data...

(5 / 11
MACHINE LEARNING
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Pratham Prasoon
Pratham Prasoon
@PrasoonPratham
Machine Learning for the Web developer in 2021.

The beginner's guide.​

🧵👇

I started machine learning as a web developer, if I can do it then anyone can.

This carefully curated thread will give you key insights into my journey and how you can make this transition, seamlessly.

(2 / 14)

"Machine learning is not what you think it is"

One of the main reasons why people find it difficult to get started with machine learning is because of the lack of information, and rightfully so.

(3 / 14)

Machine learning as a concept has existed since the 1950s, but has only become popular in recent years because of the exponential rise of advancements in computer hardware.

(4 / 14)

In short, it because of the sudden rise of this technology ,which was previously unknown to the general public, that there is a lot of misinformation around it.

(5 / 14)
MACHINE LEARNING
Caio Rolla
Caio Rolla
@caio_rolla
Starting a new project using #Angular? Here is a list of all the stuff i use to launch my projects the fastest i can.

A THREAD 👇

Have you heard about Monorepo? I created one with all my Angular (and Nest) projects using https://t.co/aY5llDtXg8.

I can share A LOT of code with it. Ex: Everytime i start a new project, i just need to import an Auth lib, that i created, and all Auth related stuff is set up.

Everyone in the Angular community knows about https://t.co/kDnunQZnxE. It's not the most beautiful component library out there, but it's good and easy to work with.

There's a bunch of state management solutions for Angular, but https://t.co/RJwpn74Qev is by far my favorite.

There's a lot of boilerplate, but you can solve this with the built-in schematics and/or with your own schematics

Are you not using custom schematics yet? Take a look at this:

https://t.co/iLrIaHVafm
https://t.co/3382Tn2k7C

You can automate all the boilerplate with hundreds of files associates with creating a new feature.
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Madison Kanna
Madison Kanna
@Madisonkanna
With hard work and determination, anyone can learn to code.

Here’s a list of my favorites resources if you’re learning to code in 2021.

👇

1. freeCodeCamp.

I’d suggest picking one of the projects in the curriculum to tackle and then completing the lessons on syntax when you get stuck. This way you know *why* you’re learning what you’re learning, and you're building things

2. https://t.co/7XC50GlIaa is a hidden gem. Things I love about it:

1) You can see the most upvoted solutions so you can read really good code

2) You can ask questions in the discussion section if you're stuck, and people often answer. Free

3. https://t.co/V9gcXqqLN6 and https://t.co/KbEYGL21iE

On stackoverflow you can find answers to almost every problem you encounter. On GitHub you can read so much great code. You can build so much just from using these two resources and a blank text editor.

4. https://t.co/xX2J00fSrT @eggheadio specifically for frontend dev.

Their tutorials are designed to maximize your time, so you never feel overwhelmed by a 14-hour course. Also, the amount of prep they put into making great courses is unlike any other online course I've seen.
MACHINE LEARNING

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The Haas / Force India argument is fascinating, and the decision of the stewards may still have consequences. The first key finding is that the stewards ruled Racing Point Force India to be a valid constructor. That means that it builds its own cars.

Haas had argued that the cars had been designed by Force India, but as Haas knows only too well, "outsourcing" is legal. The stewards decided that "there is no regulatory support for the argument that Outsourcing of Listed Parts cannot come from a former or excluded team."

In determining this, the stewards also came to the following conclusion: "In relation to the submission by the Racing Point Force India F1 Team that it is not a new team, the Stewards decide that the Racing Point Force India F1 Team is indeed a new team."

This will have a big knock on effect for Haas in its continued arguments over the rights of RPFI as relates to its eligibility and rights under the complex payments structure. Haas has argued all along that RPFI should be treated as a new team and tread the same path that it did.

TLDR? Haas lost the fight but may have won the war. In having its argument that Racing Point hadn't designed their own car thrown out, they gained clarification that RPFI was a new team.
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Thrikkangode Sree Randu Moorthy Temple
Ottappalam, Kerala. 🚩
#keralatemples

This is very unique temple as Shiva and Vishnu both are in the “linga” form in the same Garbhagriham. The Srikovil is in a circular pattern. Temple's Prathishta was done by Parasurama.


Mahadeva and Maha Vishnu in the same Srikovil is very rare Prathishta, but poojas are done separately Dhara is done to Maha Vishnu here. People suffering from “KAIVISHAM” and mental illness are fully cured on drinking a medicinal herb supplied by a nearby family here.


The root of the banyan tree around the Ayyappa Sannidhanam is said to have great powers for skin ailments.

Very ancient temple but was covered with forest for many years.
Later two persons Chakki and Chaathan, while cutting trees their axe hit on the nearby
@SriramKannan77


Vigrahams and it suddenly started bleeding. Later Devaprashnam was held and thus found out that these were murthy's of Shiva and Vishnu which was worshipped long back. And thus temple is constructed here.
Om Namah Sivaye
Om Namo Narayanaya
🕉🙏🕉
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Vibhu Vashisth
Vibhu Vashisth
@VIBHU_Tweet
महाभारत की कहानी कौन नहीं जानता।लेकिन क्या आपको पता है कि महाभारत के ज्यादातर पात्र किसी न किसी श्राप में फंसे थे।अगर ये श्राप न होते तो कदाचित महाभारत की कहानी कुछ और होती।हिन्दु पौराणिक ग्रंथों में विभिन्न श्रापों का वर्णन मिलता है व हर श्राप के पीछे कोई कहानी अवश्य होती है।


आइए आज जानते हैं महाभारत कथा में वर्णित कुछ श्रापों के बारे में।

1) राजा पाण्डु को ऋषि किन्दम का श्राप

एकबार महाराज पाण्डु शिकार खेलने वन गए।झाडियों के पीछे कुछ हिल रहा था। मृग है सोचकर राजा ने बाण चलाया जो जाकर ऋषि किन्दम और उनकी पत्नी को लगा।वे दोनो रति-क्रीड़ा में लिप्त थे।

जब राजा ने उन्हें देखा तो बहुत दुखी हुए कि ये मुझसे क्या पाप हो गया।बहुत क्षमा याचना के बाद भी किन्दम ऋषि ने पाण्डु को श्राप दे दिया कि जब भी वो किसी स्त्री को काम भावना से स्पर्श करेंगे उसी क्षण उनकी मृत्यु हो जाएगी।पश्चाताप करने, वे सिंहासन पे अन्धे राजा धृतराष्ट्र को बैठाकर...


..स्वयं अपनी रानियों कुंती व माद्री के साथ वन चले गए।पांडवों का जन्म भी कुंती को ऋषि दुर्वासा द्वारा दिए गए मंत्र से हुआ था जिसमे किसी भी देव का स्मरण कर उस देव से कुंती,पुत्र प्राप्त कर सकती थी।एक बार माद्री पे मोहित हो जब पांडु ने उसे स्पर्श किया,उसी क्षण पांडु की मृत्यु होगयी।


2) उर्वशी का अर्जुन को श्राप

महाभारत युद्ध से पहले जब अर्जुन दिव्यास्त्र प्राप्त करने स्वर्ग गए तो वहां उर्वशी नाम की अप्सरा उन पर मोहित हो गयी। अर्जुन ने जब उन्हें अपनी माता के समान बताया तो यह सुनकर उर्वशी क्रोधित हो गयी और अर्जुन को श्राप दे डाला कि तुम नपुंसक की भांति...
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Dr. Jane Clare Jones
Dr. Jane Clare Jones
@janeclarejones
So, on the subject of bonkers hyperbolic pretzeling over the Bell judgement, Grace 'destroy books I don't like & make inappropriate jokes about sterilising teenage girls' Lavery has some thoughts.

Tell me why my feminism is wrong Grace.

Oh

Well, if anyone thought the Bell judgment was going to make TRAs reconsider making massively overblown claims with no evidence backed up with nothing but a thick wadge of emotional blackmail.... HAHAHA, no one thought that.


A high court in the UK made a delimited judgment about teenager's ability to consent to puberty blockers. This puts all trans people everywhere in the world at risk.

Because if any human anywhere has any thoughts that deviate in any way from the rote line dictated by

the trans rights movement, this puts all trans people everywhere in mortal danger.

Let's be honest Grace. It doesn't put trans people at risk. It puts trans ideology at risk. Because trans ideology depends on the idea of innate gender identity, and the trans child is the

necessary material evidence of the ontology of gender identity.

That is, children are being medicalised to provide evidence to underwrite adults identities.

Nothing to see here.
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And here they are...

THE WINNERS OF THE 24 HOUR STARTUP CHALLENGE

Remember, this money is just fun. If you launched a product (or even attempted a launch) - you did something worth MUCH more than $1,000.

#24hrstartup

The winners 👇

#10

Lattes For Change - Skip a latte and save a life.

https://t.co/M75RAirZzs

@frantzfries built a platform where you can see how skipping your morning latte could do for the world.

A great product for a great cause.

Congrats Chris on winning $250!


#9

Instaland - Create amazing landing pages for your followers.

https://t.co/5KkveJTAsy

A team project! @bpmct and @BaileyPumfleet built a tool for social media influencers to create simple "swipe up" landing pages for followers.

Really impressive for 24 hours. Congrats!


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SayHenlo - Chat without distractions

https://t.co/og0B7gmkW6

Built by @DaltonEdwards, it's a platform for combatting conversation overload. This product was also coded exclusively from an iPad 😲

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CoderStory - Learn to code from developers across the globe!

https://t.co/86Ay6nF4AY

Built by @jesswallaceuk, the project is focused on highlighting the experience of developers and people learning to code.

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