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
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 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 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
Pratham Prasoon \U0001f680
Pratham Prasoon 🚀...
@PrasoonPratham
Here's how I got started with my first machine learning project and you can too.
Let's take a look.

(This thread will take from zero to being a hero in machine learning, trust me)

(1 / 22)
🧵👇


Getting started with your first machine learning project might actually much easier than it seems, if I can do it, certainly anyone can.

I did not use:
- Any Math
- An expensive computer
- Complex programming concepts

(2 / 22)

Here's what I did use:

- A free GPU on Google Colab
- Python
- TensorFlow
- Numpy
- Pandas
- Kaggle
- Scikit-Learn
- Google
- StackOverFlow

(3 / 22)

This project was actually a Kaggle challenge based on the MNIST dataset which is a collection images of 70,000 hand written digits.

You can find the dataset here👇
🔗//kaggle.com/c/digit-recognizer

(4 / 22)


Before we go over the code of this project, it is highly reccomended that you complete this free course on YouTube👇

Machine Learning foundations course
🔗//youtu.be/_Z9TRANg4c0

Code👇
🔗//colab.research.google.com/github/PrasoonPratham/Kaggle/blob/main/MNIST.ipynb

(5 / 22)
MACHINE LEARNING
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

More from 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
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
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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
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
Pratham Prasoon
Pratham Prasoon
@PrasoonPratham
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)
MACHINE LEARNING

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Time for a Thread🧵On yesterday’s “F&O Pe Charcha - Diary Of An Option Seller” with @Mitesh_Engr by @Paytm Money

Why Gain/loss ratio is important that the batting average
• More than the Win Loss ratio check the total Gain /loss ratio
• If your avg loss is 1% and avg gain is +5% then even if you are losing in 7 positions and winning in 3 out of 10 still you will be net profitable

Treat Trading as a business:
• Stick to a single strategy. Eg: A restaurateur usually wouldn’t be changing his recipe on a daily basis
• Mitesh Sir only works on BO or BD strategy, as he is a versatile trader

• Any trader should atleast experience 3 scenarios i.e. bull, bear & consolidation
• Target 4% per month i.e. 48% p.a.
• Option buying requires skill, everyday there wont be huge momentum, so buying options won’t yield good results, but high chances of falling into a bad habit

Stock Selection:
• In F&O segment there are ~150 stocks
• Not everyday all stocks will move
• Only 5-7 stocks move upside or downside
• Focus only these momentum stocks
• Check which one is moving
• Range BO/BD never disappoints. BO/BD from a rectangle (consolidation)
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Erik Torenberg
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1/“What would need to be true for you to….X”

Why is this the most powerful question you can ask when attempting to reach an agreement with another human being or organization?

A thread, co-written by @deanmbrody:


2/ First, “X” could be lots of things. Examples: What would need to be true for you to

- “Feel it's in our best interest for me to be CMO"
- “Feel that we’re in a good place as a company”
- “Feel that we’re on the same page”
- “Feel that we both got what we wanted from this deal

3/ Normally, we aren’t that direct. Example from startup/VC land:

Founders leave VC meetings thinking that every VC will invest, but they rarely do.

Worse over, the founders don’t know what they need to do in order to be fundable.

4/ So why should you ask the magic Q?

To get clarity.

You want to know where you stand, and what it takes to get what you want in a way that also gets them what they want.

It also holds them (mentally) accountable once the thing they need becomes true.

5/ Staying in the context of soliciting investors, the question is “what would need to be true for you to want to invest (or partner with us on this journey, etc)?”

Multiple responses to this question are likely to deliver a positive result.
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Vibhu Vashisth
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@VIBHU_Tweet
दधीचि ऋषि को मनाही थी कि वह अश्विनी कुमारों को किसी भी अवस्था में ब्रह्मविद्या का उपदेश नहीं दें। ये आदेश देवराज इन्द्र का था।वह नहीं चाहते थे कि उनके सिंहासन को प्रत्यक्ष या परोक्ष रुप से कोई भी खतरा हो।मगर जब अश्विनी कुमारों ने सहृदय प्रार्थना की तो महर्षि सहर्ष मान गए।


और उन्होनें ब्रह्मविद्या का ज्ञान अश्विनि कुमारों को दे दिया। गुप्तचरों के माध्यम से जब खबर इन्द्रदेव तक पहुंची तो वे क्रोध में खड़ग ले कर गए और महर्षि दधीचि का सर धड़ से अलग कर दिया।मगर अश्विनी कुमार भी कहां चुप बैठने वाले थे।उन्होने तुरंत एक अश्व का सिर महर्षि के धड़ पे...


...प्रत्यारोपित कर उन्हें जीवित रख लिया।उस दिन के पश्चात महर्षि दधीचि अश्वशिरा भी कहलाए जाने लगे।अब आगे सुनिये की किस प्रकार महर्षि दधीचि का सर काटने वाले इन्द्र कैसे अपनी रक्षा हेतु उनके आगे गिड़गिड़ाए ।

एक बार देवराज इन्द्र अपनी सभा में बैठे थे, तो उन्हे खुद पर अभिमान हो आया।


वे सोचने लगे कि हम तीनों लोकों के स्वामी हैं। ब्राह्मण हमें यज्ञ में आहुति देते हैं और हमारी उपासना करते हैं। फिर हम सामान्य ब्राह्मण बृहस्पति से क्यों डरते हैं ?उनके आने पर क्यों खड़े हो जाते हैं?वे तो हमारी जीविका से पलते हैं। देवर्षि बृहस्पति देवताओं के गुरु थे।

अभिमान के कारण ऋषि बृहस्पति के पधारने पर न तो इन्द्र ही खड़े हुए और न ही अन्य देवों को खड़े होने दिया।देवगुरु बृहस्पति इन्द्र का ये कठोर दुर्व्यवहार देख कर चुप चाप वहां से लौट गए।कुछ देर पश्चात जब देवराज का मद उतरा तो उन्हे अपनी गलती का एहसास हुआ।
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