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
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 Prasoon
Pratham Prasoon
@PrasoonPratham
Machine learning terms you must know about as a beginner.

🧵👇

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.

(2 / 11)

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.

(3 / 11)


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.

(4 / 11)


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.

(5 / 11)
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)
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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
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

More from 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
Jan Giacomelli
Jan Giacomelli
@jangiacomelli
🤔 Python decorators

What are they? How do you use them?

🧵 Let's find out 👇

1️⃣ Decorator is a function or a class that wraps another function or class modifying its behavior.

So how does that work?

The first thing to know is that everything in Python is an object - functions too


2️⃣ That means they can be passed to another function as an argument or returned from a function

Functions that take other functions as an argument are called higher-order functions


3️⃣ In Python, you can define a function inside other function - such functions are called inner functions


4️⃣ To create a decorator you just need to apply all of that together

log_enter_leave is a decorator.

my_function is the function.

To alter my_function's behavior we reassign it applying log_enter_leave decorator.
MACHINE LEARNING
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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
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
Santiago
Santiago
@svpino
The 8-step quick-start guide to learn Machine Learning.

🧵👇

1⃣ Start with Python 🐍

Yes, you can do other languages, but Python is by far the most straightforward option.

👇

2⃣ Get familiar with numpy, pandas, and matplotlib

These three libraries are probably the most common Python libraries you'll have to use every day.

(Even if you don't end up doing machine learning, these libraries are awesome and useful.)

👇

3⃣ Start using notebooks

Look into Jupyter or Google Colab.

Notebooks are essential for data scientists and machine learning practitioners. Most of the code you'll read and write will be in notebooks.

👇

4⃣ Find a problem (already solved)

In my opinion, the best way to start is by working through a problem —especially when you can learn from its solution.

Start with something simple. I usually recommend "Titanic" from Kaggle.

👇
MACHINE LEARNING , STARTING MACHINE LEARNING

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வேதத்துக்குப் பொருள் அருளிச் செய்த படலம்


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SHAKTA DHARMA AND THEIR ACHARYAS

Śhākta or Shakti or Devi pooja is not a new upasana. It is being carried out right from the time of Vedic period. Puran's tell us that Rishi Vashishth would worship Tara devi. Parshuram ji worshipped Tripura devi,


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Not much is known about the Śhākta saints in other parts of India in the middle ages.

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🌺कैसे बने गरुड़ भगवान विष्णु के वाहन और क्यों दो भागों में फटी होती है नागों की जिह्वा🌺

महर्षि कश्यप की तेरह पत्नियां थीं।लेकिन विनता व कद्रु नामक अपनी दो पत्नियों से उन्हे विशेष लगाव था।एक दिन महर्षि आनन्दभाव में बैठे थे कि तभी वे दोनों उनके समीप आकर उनके पैर दबाने लगी।


प्रसन्न होकर महर्षि कश्यप बोले,"मुझे तुम दोनों से विशेष लगाव है, इसलिए यदि तुम्हारी कोई विशेष इच्छा हो तो मुझे बताओ। मैं उसे अवश्य पूरा करूंगा ।"

कद्रू बोली,"स्वामी! मेरी इच्छा है कि मैं हज़ार पुत्रों की मां बनूंगी।"
विनता बोली,"स्वामी! मुझे केवल एक पुत्र की मां बनना है जो इतना बलवान हो की कद्रू के हज़ार पुत्रों पर भारी पड़े।"
महर्षि बोले,"शीघ्र ही मैं यज्ञ करूंगा और यज्ञ के उपरांत तुम दोनो की इच्छाएं अवश्य पूर्ण होंगी"।


महर्षि ने यज्ञ किया,विनता व कद्रू को आशीर्वाद देकर तपस्या करने चले गए। कुछ काल पश्चात कद्रू ने हज़ार अंडों से काले सर्पों को जन्म दिया व विनता ने एक अंडे से तेजस्वी बालक को जन्म दिया जिसका नाम गरूड़ रखा।जैसे जैसे समय बीता गरुड़ बलवान होता गया और कद्रू के पुत्रों पर भारी पड़ने लगा


परिणामस्वरूप दिन प्रतिदिन कद्रू व विनता के सम्बंधों में कटुता बढ़ती गयी।एकदिन जब दोनो भ्रमण कर रहीं थी तब कद्रू ने दूर खड़े सफेद घोड़े को देख कर कहा,"बता सकती हो विनता!दूर खड़ा वो घोड़ा किस रंग का है?"
विनता बोली,"सफेद रंग का"।
तो कद्रू बोली,"शर्त लगाती हो? इसकी पूँछ तो काली है"।
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