For example👇
(2 / 17)
Everything you need to know about the math for machine learning as a beginner.
🧵👇
- Object oriented programming in Python : Classes, Objects, Methods
- List slicing
- String formatting
- Dictionaries & Tuples
- Basic terminal commands
- Exception handling
(3 / 17)
🔗Basics: youtube .com/watch?v=rfscVS0vtbw
🔗Intermediate :youtube .com/watch?v=HGOBQPFzWKo
(4 / 17)
It is 100% compulsory.
(5 / 17)
(6 / 17)
(7 / 17)
For instance I was recently competing in a kaggle challenge.
(8 / 17)
(9 / 17)
- Linear Algebra
- Calculus
- Trigonometry
- Algebra
- Statistics
- Probability
Now here are the math resources and a brief description about them.
(10 / 17)
> 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)
> This website gives you an interactive to learn statistics and probability
🔗seeing-theory. brown. edu/basic-probability/index.html
(12 / 17)
> 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)
> 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)
>The resource you must refer to when you forget something or want to revise a topic.
🔗khanacademy. org/math
(15 / 17)
More from Pratham Prasoon
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)
(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)
More from Machine learning
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
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
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.
You May Also Like
And here they are...
THE WINNERS OF THE 24 HOUR STARTUP CHALLENGE
Remember, this money is just fun. If you launched a product (or even attempted a launch) - you did something worth MUCH more than $1,000.
#24hrstartup
The winners 👇
#10
Lattes For Change - Skip a latte and save a life.
https://t.co/M75RAirZzs
@frantzfries built a platform where you can see how skipping your morning latte could do for the world.
A great product for a great cause.
Congrats Chris on winning $250!
#9
Instaland - Create amazing landing pages for your followers.
https://t.co/5KkveJTAsy
A team project! @bpmct and @BaileyPumfleet built a tool for social media influencers to create simple "swipe up" landing pages for followers.
Really impressive for 24 hours. Congrats!
#8
SayHenlo - Chat without distractions
https://t.co/og0B7gmkW6
Built by @DaltonEdwards, it's a platform for combatting conversation overload. This product was also coded exclusively from an iPad 😲
Dalton is a beast. I'm so excited he placed in the top 10.
#7
CoderStory - Learn to code from developers across the globe!
https://t.co/86Ay6nF4AY
Built by @jesswallaceuk, the project is focused on highlighting the experience of developers and people learning to code.
I wish this existed when I learned to code! Congrats on $250!!
THE WINNERS OF THE 24 HOUR STARTUP CHALLENGE
Remember, this money is just fun. If you launched a product (or even attempted a launch) - you did something worth MUCH more than $1,000.
#24hrstartup
The winners 👇
#10
Lattes For Change - Skip a latte and save a life.
https://t.co/M75RAirZzs
@frantzfries built a platform where you can see how skipping your morning latte could do for the world.
A great product for a great cause.
Congrats Chris on winning $250!
#9
Instaland - Create amazing landing pages for your followers.
https://t.co/5KkveJTAsy
A team project! @bpmct and @BaileyPumfleet built a tool for social media influencers to create simple "swipe up" landing pages for followers.
Really impressive for 24 hours. Congrats!
#8
SayHenlo - Chat without distractions
https://t.co/og0B7gmkW6
Built by @DaltonEdwards, it's a platform for combatting conversation overload. This product was also coded exclusively from an iPad 😲
Dalton is a beast. I'm so excited he placed in the top 10.
#7
CoderStory - Learn to code from developers across the globe!
https://t.co/86Ay6nF4AY
Built by @jesswallaceuk, the project is focused on highlighting the experience of developers and people learning to code.
I wish this existed when I learned to code! Congrats on $250!!
