How to implement Naive Bayes from scratch with Python

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In the 6th lesson of the Machine Learning from Scratch course, we will learn how to implement the Naive Bayes algorithm.

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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#MachineLearning #DeepLearning

AssemblyAI

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the coding is really awesome . i love it.. please would you provide the rest of algorithms of machine learning like adaboost, XGBoost etc and also deep learning ...

azharafridi

This won’t work on imbalance dataset. only class dominate which has more value, because mean and variance would be always higher the other class

rashidkhan

This video is way too fast and not at all explained

RastiMuzic

And what about head on over to implement kalman filter or particle filter from scratch ? Lets take forex data:)

lubosnagy

"then we can simplify this a little bit so we can first get rid of p of x because this depends not on y at all so just throw this away " What?? That's then totally not the same formula, no clue what's happening here.
" all these probabilities here are values between 0 and 1 and if we multiply this then the number can become very small and we can run into inaccuracies so for this we apply a little trick instead of the product we do a sum and then we apply the logarithm so if you apply the logarithm we can change the product with a sum and then this is the final formula to get y", Ma man u just created a completely different formula and didn't provide any actual explanations or even maths references to check.

this is heavily poorly explained and rushed. :/

business_central

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