How to implement Perceptron from scratch with Python

In the 8th lesson of the Machine Learning from Scratch course, we will learn how to implement the Perceptron algorithm.
You can find the code here: github.com/AssemblyAI-Example...
Previous lesson: • How to implement PCA (...
Next lesson: • How to implement SVM (...
Welcome to the Machine Learning from Scratch course by AssemblyAI.
Thanks to libraries like Scikit-learn we can use most ML algorithms with a couple of lines of code. But knowing how these algorithms work inside is very important. Implementing them hands-on is a great way to achieve this.
And mostly, they are easier than you’d think to implement.
In this course, we will learn how to implement these 10 algorithms.
We will quickly go through how the algorithms work and then implement them in Python using the help of NumPy.
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[@alfonsoramirezelorriaga1153]

I liked that the mathematical explanation is very clear. Also, for the python implementation you wrote the code from scratch, rather than copy past it, and walked the viewer through each line. Thank you.

[@zmm978]

The ending escalated very quickly, lol

[@rizzbod]

Thnx buddy! Clean explanation

[@ojaswighate2588]

Thank you for sharing!!

[@AssemblyAI]

Thanks for watching!

[@jerielopvp]

And how would you implement the multiclass one ?

[@fazulf1054]

Nice explanation

[@marco8673]

during fittig linear_output = np.dot(x_i,self.weight) +self.bias during prediction linear_output = np.dot(X,self.weight) +self.bias X and x_i are two type of different object, so during the fitting linear_output is the prediction is done on one item, and during the prediction is a prediction of a list of item right?

[@kshitijnishant4968]

What's different in between this and Logit? Both scripts feel the same?

[@mohammedamirjaved8418]

Love you man...😘

[@mioszmephir2926]

thanks for help

[@maryamaghili1148]

why you did not write the loop in vectorized form like what you did in regression models? what is the difference?

[@emrek1]

He is updating the weights and biases for each data sample. So at each iteration he makes the prediction with the updated weight. This is stochastic gradient. It can be done in the other way as you said also. The weights will be updated once after an epoch in that case.

[@georulez89]

didnt know messi was into teaching python

[@MustafaAli-ve1vm]

accuracy 100%?? that should be suspicious

[@EliSpizzichino]

not in a binary classifier