Being Magarni

#WordPress #SelfTaught #WebDeveloper

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[Being Magarni @GMagarni]

कुनै काम पनी मन देखिनै गरेको छ
भनी मन सम्म पुग्छ नै।

[Being Magarni @GMagarni]

@fundedxofficial 3

[ＤＯＣＴＯＵＲ @wakawaka_doctor]

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All the best!

[Csaba Kissi @csaba_kissi]

6 APIs for your next project

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3. Memes API
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5. Bored API
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[Akshay 🚀 @akshay_pachaar]

PCA from scratch using Python 🔥

Principal Component Analysis is a powerful technique for dimensionality reduction and data visualisation.

-- step by step explanation with code --

Let's go! 🚀

[Hasan Toor @hasantoxr]

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[Akshay 🚀 @akshay_pachaar]

Stanford offers FREE Data Science Education!

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[Hasan Toor @hasantoxr]

Harvard and Stanford have free resources for resumes, CV, and cover letters.

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[Mark Gadala-Maria @markgadala]

X sent out millions in creator payouts today.

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[Saheed Kolawole @temabef]

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[Machine Learning Community ⭐️ @c4ml_]

Tableau is very powerful and an important tool for any Data Analyst🔥

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[Swapna Kumar Panda @swapnakpanda]

Practice for FREE and crack your interviews
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- DSA

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[Sumanth @Sumanth_077]

Statistics 110: Probability by Harvard University.

Inarguably, one of the best probability courses on the web!

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[Shreya @_Shreya_Trivedi]

Basic GIT Commands CHEAT SHEET 👇🔥

[Pratham @Prathkum]

Generative AI can change the way you work:

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Text to video

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[Akshay 🚀 @akshay_pachaar]

Linear Regression clearly Explained!🚀

Linear Regression models relationship between a dependant variable (y) & two or more independent variables (x1, x2 ...)

❗️For the sake of simplicity we discuss linear regression with a single independent variable.

Mathematical Representation:

y=mx+c

where:
- y is the dependent variable
- x is the independent variable
- m is the slope of the line
- c is the y intercept

Cost Function:

❗️The goal is to find the best values for m and c that minimize the error between the predicted values & the actual values for all data points.

This is how the cost function looks, a mean squared error:

MSE= 1/N∑(yi​−(m*xi​+c))**2

Where:

- N is the number of data points.
- yi​ is the actual value for the ith data point.
- mxi​+c is the predicted value for the ith data point.

How it works (Optimisation)❓

To minimize the MSE, we can use gradient descent.

The basic idea is to:

- Start with some initial value of m & c
- Compute the gradient of the MSE w.r.t. both m & c.
- Update m & c in the direction of the negative gradient.

Implementation from Scratch:

Now that we understand how things work it's fairly simple to implement linear regression in Python!

Check this out👇

That's a wrap, If you interested in:

- Python 🐍
- Machine Learning 🤖
- Maths for ML 🧮
- MLOps 🛠
- CV/NLP 🗣
- LLMs 🧠

Find me → @akshay_pachaar ✔️
Everyday, I share tutorials on above topics!

Cheers!🥂

[Swapna Kumar Panda @swapnakpanda]

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[Data Science Dojo @DataScienceDojo]

💡 Different types of machine learning!
Source: Data Science School

#MachineLearning #DataScience

[Param @Param_eth]

10 websites helps you to learn Web3 Development for FREE:

• learnweb3
• useweb3. xyz
• buildspace .so
• metaschool. so
• cryptozombies. io
• questbookapp .xyz
• freecodecamp .org
• Alchemy University
• solidity-by-example
• ethernaut.openzeppelin. com

Happy web3 :)