I use these resources and they will (hopefully) help you in understanding the theoretical aspects of machine learning very well.
Do you want to learn the maths for machine learning but don't know where to start?
This thread is for you.
🧵👇
I use these resources and they will (hopefully) help you in understanding the theoretical aspects of machine learning very well.
Read this thread for more details👇
https://t.co/sSN3jdxDwK
Are you planning to learn Python for machine learning this year?
— Pratham Prasoon (@PrasoonPratham) February 13, 2021
Here's everything you need to get started.
\U0001f9f5\U0001f447
- 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)
To manipulate and represent data.
- Calculus
To train and optimize your machine learning model, this is very important.
> A series of videos that go over how neural networks work with approach visual, must watch.
🔗youtu.be/aircAruvnKk
> This website helps you learn statistics and probability in an intuitive way.
🔗seeing-theory.brown.edu/basic-probability/index.html
> 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
https://t.co/3H7U2HJgTd
This is a beginner-friendly introduction to:
— Pratham Prasoon (@PrasoonPratham) January 24, 2021
Linear Algebra for Machine Learning.
\U0001f9f5\U0001f447
> 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
>You'll find a course on everything here! Khan Academy is the first place I'll go to when I want to learn something.
🔗khanacademy.org/math
> A beautiful series on calculus, makes everything seem super simple.
🔗youtube.com/watch?v=WUvTyaaNkzM&list=PL0-GT3co4r2wlh6UHTUeQsrf3mlS2lk6x
More from Pratham Prasoon
A list of my favourite tutorials for learning Python as a beginner.
🧵 👇🏻
All the tutorials below include the basics like installation, variables etc.
I've also listed out the key highlights of each tutorial so that it is easy for you decide which one to pick.
Before going through these tutorials I would highly suggest you to go through this thread if you are a complete
The Full Python Course from learn with Python with Rune
key highlights
- 17 part course
- Jupyter notebooks
- Free eBook included
Duration: 8
The Python Beginner's course on FreeCodeCamp's YouTube Channel.
Key highlights
- Building a casic calculator
- Mad Libs Game
- Slightly advanced concepts like inheritance, Classes etc.
Duration: 4
🧵 👇🏻
All the tutorials below include the basics like installation, variables etc.
I've also listed out the key highlights of each tutorial so that it is easy for you decide which one to pick.
Before going through these tutorials I would highly suggest you to go through this thread if you are a complete
Are you planning to learn Python for machine learning this year?
— Pratham (@PrasoonPratham) February 13, 2021
Here's everything you need to get started.
\U0001f9f5\U0001f447
The Full Python Course from learn with Python with Rune
key highlights
- 17 part course
- Jupyter notebooks
- Free eBook included
Duration: 8
The Python Beginner's course on FreeCodeCamp's YouTube Channel.
Key highlights
- Building a casic calculator
- Mad Libs Game
- Slightly advanced concepts like inheritance, Classes etc.
Duration: 4
More from Machine learning
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.
❓ 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
I'm starting a Twitter series on #FoundationsOfML. Today, I want to answer this simple question.
— Alejandro Piad Morffis (@AlejandroPiad) January 12, 2021
\u2753 What is Machine Learning?
This is my preferred way of explaining it... \U0001f447\U0001f9f5
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.
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THREAD PART 1.
On Sunday 21st June, 14 year old Noah Donohoe left his home to meet his friends at Cave Hill Belfast to study for school. #RememberMyNoah💙
He was on his black Apollo mountain bike, fully dressed, wearing a helmet and carrying a backpack containing his laptop and 2 books with his name on them. He also had his mobile phone with him.
On the 27th of June. Noah's naked body was sadly discovered 950m inside a storm drain, between access points. This storm drain was accessible through an area completely unfamiliar to him, behind houses at Northwood Road. https://t.co/bpz3Rmc0wq
"Noah's body was found by specially trained police officers between two drain access points within a section of the tunnel running under the Translink access road," said Mr McCrisken."
Noah's bike was also found near a house, behind a car, in the same area. It had been there for more than 24 hours before a member of public who lived in the street said she read reports of a missing child and checked the bike and phoned the police.
On Sunday 21st June, 14 year old Noah Donohoe left his home to meet his friends at Cave Hill Belfast to study for school. #RememberMyNoah💙
He was on his black Apollo mountain bike, fully dressed, wearing a helmet and carrying a backpack containing his laptop and 2 books with his name on them. He also had his mobile phone with him.
On the 27th of June. Noah's naked body was sadly discovered 950m inside a storm drain, between access points. This storm drain was accessible through an area completely unfamiliar to him, behind houses at Northwood Road. https://t.co/bpz3Rmc0wq
"Noah's body was found by specially trained police officers between two drain access points within a section of the tunnel running under the Translink access road," said Mr McCrisken."
Noah's bike was also found near a house, behind a car, in the same area. It had been there for more than 24 hours before a member of public who lived in the street said she read reports of a missing child and checked the bike and phoned the police.
