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
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
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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Pratham Prasoon
Pratham Prasoon
@PrasoonPratham
This is a step-by-step guide on getting started with Python as a total beginner.

(includes free resources and everything else you need to get started)
🧵👇

Before we begin, I want to congratulate you on your decision to learn how to code using Python.

I still remember how I wrote my first piece of code 6 years and all the amazing and cool things I've been able to do with it ever since.

(2 / 24)

Make sure to keep in mind that it is probably best for you to keep your expectations in check.

Don't expect to make AAA games or state of the art machine learning models in a week.

(3 / 24)

Programming is not something that you can learn in a single week, it takes consistent effort and dedication over time to get good at it.

With all that being said, let's dive straight in.

(4 / 24)

In order to write Python code, you'll need to install Python on your system.

Linux and macOS users can skip this step because they come pre-installed with Python.

Download link: https://t.co/KSZ4Qd6CNk

(5 / 24)
TECH
Pratham
Pratham
@PrasoonPratham
This is a beginner's guide on mining cryptocurrencies.

🧵 👇🏻


Mining 101

Typically, when you transfer money using a service like Paypal, they take a small cut for facilitating the exchange.

In cryptocurrencies, people like you and me act as Paypal and facilitate exchanges of cryptocurrency. We get a cut for this just like Paypal did.

In order to make these transactions happen, our computers need to do some calculations which requires a lot of computational power.

A GPU or a Graphics Processing Unit which is typically marketed for gaming workloads can be used to mine cryptocurrencies.

Why do you need a GPU?

Today, there are so many miners that the "difficulty" of mining cryptocurrencies has skyrocketed, which basically means it takes a lot of computational power to mine crypto which GPUs can provide and CPUs cannot.

(❗ This is an oversimplification)

If you are interested in the inner workings of how blockchain and cryptocurrency, then I highly suggest that you read this thread by @oliverjumpertz
CRYPTOCURRENCIES GUIDE
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

More from Machine learning

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
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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Unknown \U0001f464
Unknown 👤
@console_404
10 PYTHON 🐍 libraries for machine learning.

Retweets are appreciated.
[ Thread ]


1. NumPy (Numerical Python)

- The most powerful feature of NumPy is the n-dimensional array.

- It contains basic linear algebra functions, Fourier transforms, and tools for integration with other low-level languages.

Ref: https://t.co/XY13ILXwSN


2. SciPy (Scientific Python)

- SciPy is built on NumPy.

- It is one of the most useful libraries for a variety of high-level science and engineering modules like discrete Fourier transform, Linear Algebra, Optimization, and Sparse matrices.

Ref: https://t.co/ALTFqM2VUo


3. Matplotlib

- Matplotlib is a comprehensive library for creating static, animated, and interactive visualizations in Python.

- You can also use Latex commands to add math to your plot.

- Matplotlib makes hard things possible.

Ref: https://t.co/zodOo2WzGx


4. Pandas

- Pandas is for structured data operations and manipulations.

- It is extensively used for data munging and preparation.

- Pandas were added relatively recently to Python and have been instrumental in boosting Python’s usage.

Ref: https://t.co/IFzikVHht4
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
Santiago
Santiago
@svpino
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

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Vaarahi is one of the Saivite Shakti deities with the group of goddesses known as SAPTAMATAS.
They are: Brahmi, Maheshwari, Kaumari, Vaishnavi, Vaarahi, Mahendri and Chamundi. 1/n


They are the Shakti (female) equivalents of their male counterparts. Vaarahi is the Shakti of Bhairavar who is one of the Amsam of Bhairavar.Created from the boar avatara of Vishnu, Vaarahi is often associated with Goddess Kamala (one of the Mahavidyas). 2/n


She is also called Dandini (Lady Commander of the forces of the Mother-Goddess, symbolizing the might of SriVidya). As Vaarahi ("hog-faced" or "the great consumer") she destroys evil forces that obstructs the devotees' progress, paralyses the enemies 3/n


and led the devotees ultimately to SriVidyas. She is also called Panchami). With Kurukalla, she is accorded the parental status to Sri-Chakra (Lalita Tripura-Sundari). While Kurukalla represents the full moon, Vaarahi represents the new-moon. 4/4

Jai Maa Varahi 🙏


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What is best way to sell strangle in Banknifty on an intraday basis
Simple
at 9:45 Open Option chain and check In which strike highest OI change in call side and put side

Sell both option with combine SL
Enjoy easy and simple intraday trading strategy
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1)King Porus was a famous Indian king, his real name was #Purushotama. He belonged to PURU tribe mentioned in #RIG_VEDA, the Ancient books of India.


2) Porus ruled the Punjab region and expanded his kingdom between the Jhelum River and Chenab River in the Indian Subcontinent. He was a legendary warrior and more war skills. He made the army most powerful with war skills. Even Alexander had also made difficulty to defeat Porus.

3)Word of Mouth Oral tradition in #Mohyal_Brahmin families since ancient times has kept the Great Saga of their Great Elder known to them as Maharaja “ #Puru” alive.

4) When #Alexander the Great invaded India in 326 BC he was challenged near Taxila by a mere chieftain of the Area. He was King #Porus, a #Vaid_Mohyal. After his encounter with Porus, Alexander met with more resistance near Multan where the forces were predominantly #Mohyal.

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Rani Chennamma of Kittur was one of the first women rulers of India who fought against the might of the British Empire in 1824. Rani Chennamma was born in 1778 in a small village of Kakati in Belgaon distt of the State of Karnataka.


She was born into the Lingayat Community to a man named Dhulappa Desai. From a very young age she was trained in horse riding,sword fighting, archery and diplomacy. At a very young age of 15 yrs, she was married to the King of Kittur and became the Queen of Kittur.


It is said that King of Kittur Mallasarja was once moving around in the neighbouring kingdoms to find support to fight Tipu Sultan. It is then, when he came across Chennamma and he immediately got attracted to her sheer confidence and bravery.

Rani Chennamma and King Mallasarja had a son together who died very soon after the death of King Mallasarja in 1816. As the Queen of Kittur, Rani Chennamma immediately adopted Shivalingappa as her son and declared him the heir to the throne of Kittur.

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Here I will share what I believe are essentials for anybody who is interested in stock markets and the resources to learn them, its from my experience and by no means exhaustive..

First the very basic : The Dow theory, Everybody must have basic understanding of it and must learn to observe High Highs, Higher Lows, Lower Highs and Lowers lows on charts and their

Even those who are more inclined towards fundamental side can also benefit from Dow theory, as it can hint start & end of Bull/Bear runs thereby indication entry and exits.


Next basic is Wyckoff's Theory. It tells how accumulation and distribution happens with regularity and how the market actually

Dow theory is old but
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