BC MACHINE LEARNING
Saved by @Alex1Powell

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

(6 / 17)
Math for machine learning should come after you have worked on some projects, doesn't have to a complex one at all, but one that gives you a taste of how machine learning works in the real world.

(7 / 17)
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 challenge.

(8 / 17)
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 / 17)
The topics of math you'll have to focus on
- Linear Algebra
- Calculus
- Trigonometry
- Algebra
- Statistics
- Probability

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

(10 / 17)
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 / 17)
Seeing Theory
> This website gives you an interactive to learn statistics and probability

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

(12 / 17)
Gilbert Strang lectures on Linear Algebra (MIT)
> They're 15 years old but still 100% relevant today!
Despite the fact these lectures are for freshman college students ,I found it very easy to follow.

🔗youtube. com/playlist?list=PL49CF3715CB9EF31D

(13 / 17)
Essence of Linear Algebra
> A beautifully crafted set 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 / 17)
Khan Academy
>The resource you must refer to when you forget something or want to revise a topic.

🔗khanacademy. org/math

(15 / 17)
Essence of calculus
> A beautiful series on calculus, makes everything seem super simple

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

(16 / 17)
The math for Machine learning e-book
> This is a book aimed for someone who knows a decent amount of high school math like trignometry, calculus etc.

I suggest reading this after having the fundamentals down on khan academy.

🔗mml-book. github .io

(17 / 17)

More from Pratham Prasoon

Pratham Prasoon
Pratham Prasoon
@PrasoonPratham
Can you get a job in data science and machine learning without a college degree?

🧵👇

Short Answer: Yes.

Long Answer, keep reading 👇

(Advice from industry experts who I talked to.)

Companies are looking for people who add value.
In order to add value, you'll need skills. Simple as that.

Get the skills to provide value and you'll get the job.

In the age of the internet where everything is pretty much free, why do college degrees matter?

A college degree makes it easier to get the skills to get a job in machine learning or data science.

College degrees also help you make many useful connections and provides many opportunities in the form of internships and whatnot.

College degrees have their own place
MACHINE LEARNING
Pratham Prasoon
Pratham Prasoon
@PrasoonPratham
This is how I got three job offers as a 16-year-old high school student.

( and why I rejected them )
🧵👇

June 2020.

I receive a DM from a college student in California. The message read something like this "Hey Pratham, I am one of the co-founders of XYZ Inc, a robotics company, we are looking to add enthusiastic people to our team, would you be interested?"

(2 / 11)

It was the first time I had ever been offered a job, so I said Yes! (I did reject it later)

There were a couple of interview rounds I had to go through. None of them included technical questions, which was interesting.

(3 / 11)

After the first interview, I saw the list of the roles I could apply for. I realized how little I knew, terms like "Ui/UX, ROS(Robot operating system)", at that time, I was overwhelmed.

(4 / 11)

I knew that this wasn't a job for me. I decided that I would reject this job role even if I cleared the second round, which I ended up doing.

(5 / 11)
LIFE
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Pratham
Pratham
@PrasoonPratham
I remember how hard it was to find resources for machine learning a couple of years ago.

Now we have so many of them, its impossible for you to not find what you're looking for.

Here are some of them 🧵

1️⃣ Kaggle

My go to place for finding datasets and code snippets, also a great place for discussions.


2️⃣ Papers with code

A collection of the latest and greatest research papers and the best part, a github repository with the code is included!


3️⃣ Kdnuggets

Guides, tutorials and articles on everything AI, Analytics, Big Data, Data Mining, Data Science, and Machine Learning.


4️⃣ Towards Data Science

A blog about data science with accessible and well written articles.
CODING
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
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

More from 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
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
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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👇

(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
Ram Bhupatiraju
Ram Bhupatiraju
@RamBhupatiraju
Happy 2⃣0⃣2⃣1⃣ to all.🎇

For any Learning machines out there, here are a list of my fav online investing resources. Feel free to add yours.

Let's dive in.
⬇️⬇️⬇️

Investing Services

✔️ @themotleyfool - @TMFStockAdvisor & @TMFRuleBreakers services

✔️ @7investing

✔️ @investing_city
https://t.co/9aUK1Tclw4

✔️ @MorningstarInc Premium

✔️ @SeekingAlpha Marketplaces (Check your area of interest, Free trials, Quality, track record...)

General Finance/Investing

✔️ @morganhousel
https://t.co/f1joTRaG55

✔️ @dollarsanddata
https://t.co/Mj1owkzRc8

✔️ @awealthofcs
https://t.co/y81KHfh8cn

✔️ @iancassel
https://t.co/KEMTBHa8Qk

✔️ @InvestorAmnesia
https://t.co/zFL3H2dk6s

✔️

Tech focused

✔️ @stratechery
https://t.co/VsNwRStY9C

✔️ @bgurley
https://t.co/NKXGtaB6HQ

✔️ @CBinsights
https://t.co/H77hNp2X5R

✔️ @benedictevans
https://t.co/nyOlasCY1o

✔️

Tech Deep dives

✔️ @StackInvesting
https://t.co/WQ1yBYzT2m

✔️ @hhhypergrowth
https://t.co/kcLKITRLz1

✔️ @Beth_Kindig
https://t.co/CjhLRdP7Rh

✔️ @SeifelCapital
https://t.co/CXXG5PY0xX

✔️ @borrowed_ideas
MACHINE LEARNING , ALL
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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John Myles White
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This post is pretty bizarre, but it manages to hit on so many false beliefs that I've seen hurt junior data scientists that it deserves some explicit

(1) The notion that R is well-suited to "building web applications" seems totally out of left field. I don't feel like most R loyalists think this is a good idea, but it's worth calling out that no normal company will be glad you wrote your entire web app in R.

(2) It is true that Python had some issues historically with the 2-to-3 transition, but it's not such a big deal these days. On the flip side, I have found interesting R code that doesn't run in modern R interpreters because of changes in core operations (e.g. assignment syntax).

(3) "Most of the time we only need a latest, working interpreter with the latest packages to run the code" -- this is where things get real and reveal some things that hurt data scientists. If this sentence is true, it's likely because you don't share code with coworkers.

(3) Really is a broader issue in data science: people only think of what they need to do their work if no one else existed and code was never maintained. Junior data scientists almost always operate on projects they start from scratch and don't have to maintain for long.
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The '60s at 60
The '60s at 60
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"The MAD Primer of Bigots, Extremists and Other Loose Ends," from September 1969.

Seen a couple of these panels make the rounds from time to time, so here's the complete set of 10 (something to amuse and/or offend almost everyone).


Chapter 1: The Super Patriot


Chapter 2: The Ku Klux Klansman


Chapter 3: The American Student


Chapter 4: The Right-Wing Extremist
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Jaya_Upadhyaya
Jaya_Upadhyaya
@Jayalko1
SRISAILAM: KASHI OF THE SOUTH (AP)
#andhratemples

The divine abode of Mallikarjuna Swamy (Jyotirlinga) and Devi Brahmaramba (Shaktipeeth).

When Ganesha was married before Kartikeya, he left in anger to Kraunch mountain. Shiva and Parvati also followed him there.


Shiva assumed the form of Jyotirlinga and resided on the mountain by the name of Mallikarjuna. Mallika means Parvati while Arjuna is another name of Shiva
It is also said that once, a princess Chandravathi, saw a cow shedding milk on a rock resembling a shivlingam.
@DubeyYash1911


She started praying to the lingam daily and used to offer jasmine (mallika pushpam) daily. Pleased Shiva appeared before her and agreed to wear jasmine garland (mallika pushpmala) on his head permanently. Since then, Shiva became known as Mallikarjun Swamy.


At this place, Shakti took the form of Bhramaram (bee) to kill Arunasur and settled as Bhramaramba Devi. The place is a Shakti Peeth as the neck portion of Devi Sati fell here.

It is said that by merely seeing the hill, one is emancipated from all his sins and worries.
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sriram
sriram
@SriramKannan77
Swami Ramanujar:
Important personalities in Sri Ramanujas Life :
🌸ASURI KESHAVA SOMAYAJI-Father of Sri Ramanujar .Asuri keshava somayaji was a learned brahmana living in sriperumbudur.


🌸KANTHIMATHI-Mother of Sri Ramanuja. Kanthimathi was the sister of sri periya thirumalai nambi .
🌸SRI YAMUNACHARYA-Acharya of Sri Ramanuja.Also known as Alavandar.
🌸THIRUKOKOSHTIYUR NAMBI-Also known as Ghosti Purna.He instructed the Rahasya Traya to Sri Ramanujar.

He lived in thirukoshtiyur.
🌸THIRUKKACHI NAMBI-Kanchi purna was born in puvirunthavalli near kanchipuram and met the young ramanuja at an important time.He did the service of Lord Varadha.

🌸THIRUVARANGA PERUMAL ARAIYAR-Important acharya lived in srirangam and rendered the service of playing music for lord Sriranganatha.

🌸PERIYA THIRUMALAI NAMBI-He was the maternal uncle of Sri Ramanuja.He taught sri ramanuja about the epic of sri ramayana and other ithihaas and puranas.Served sri venkateswara at tirumala.
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Vibhu Vashisth
Vibhu Vashisth
@VIBHU_Tweet
हिमालय पर्वत की एक बड़ी पवित्र गुफा थी।उस गुफा के निकट ही गंगा जी बहती थी।एक बार देवर्षि नारद विचरण करते हुए वहां आ पहुंचे।वह परम पवित्र गुफा नारद जी को अत्यंत सुहावनी लगी।वहां का मनोरम प्राकृतिक दृश्य,पर्वत,नदी और वन देख उनके हृदय में श्रीहरि विष्णु की भक्ति अत्यंत बलवती हो उठी।


और देवर्षि नारद वहीं बैठकर तपस्या में लीन हो गए।इन्द्र नारद की तपस्या से घबरा गए।उन्हें हमेशा की तरह अपना सिंहासन व स्वर्ग खोने का डर सताने लगा।इसलिए इन्द्र ने नारद की तपस्या भंग करने के लिए कामदेव को उनके पास भेज दिया।वहां पहुंच कामदेव ने अपनी माया से वसंतऋतु को उत्पन्न कर दिया।


पेड़ और पौधों पर रंग बिरंगे फूल खिल गए और कोयलें कूकने लगी,पक्षी चहकने लगे।शीतल,मंद,सुगंधित और सुहावनी हवा चलने लगी।रंभा आदि अप्सराएं नाचने लगीं ।किन्तु कामदेव की किसी भी माया का नारद पे कोई प्रभाव नहीं पड़ा।तब कामदेव को डर सताने लगा कि कहीं नारद क्रोध में आकर मुझे श्राप न देदें।

जैसे ही नारद ने अपनी आंखें खोली, उसी क्षण कामदेव ने उनसे क्षमा मांगी।नारद मुनि को तनिक भी क्रोध नहीं आया और उन्होने शीघ्र ही कामदेव को क्षमा कर दिया।कामदेव प्रसन्न होकर वहां से चले गए।कामदेव के चले जाने पर देवर्षि के मन में अहंकार आ गया कि मैने कामदेव को हरा दिया।

नारद फिर कैलाश जा पहुंचे और शिवजी को अपनी विजयगाथा सुनाई।शिव समझ गए कि नारद अहंकारी हो गए हैं और अगर ये बात विष्णु जी जान गए तो नारद के लिए अच्छा नहीं होगा।ये सोचकर शिवजी ने नारद को भगवन विष्णु को ये बात बताने के लीए मना किया। परंतु नारद जी को ये बात उचित नहीं लगी।
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