Scikit-learn Crash Course - Machine Learning Library for Python

i hate python so much, just errors after errors

This is compelling writing. If the subject fascinates you, a subsequent book with similar themes would be beneficial. "From Bytes to Consciousness: A Comprehensive Guide to Artificial Intelligence" by Stuart Mills

Rime series needed these Polynomial parameters, i think. Cool tutorial though!

Here in 2023, just fyi: the Boston data set has been shown to have ethical problems and the creators of scikit-learn strongly suggest against using the data set.

Very helpful! Thank you!

Thank you for uploading this video!

where is the dataset ???

The way each dataset complements the associated pitfall you want to bring up at a given moment... wow. What an amazing intro -- it must have taken a lot of forethought and behind the scenes organization to make the flow of this video series seem so effortless. THANK YOU!!

the explanations are well detailed, this really helps with understanding the library and know exactly what to use and where to use it. You have helped a great community of beginners. 🙏🏾🙏🏾🙏🏾🙏🏾🙏🏾

Great video!

God!
"`load_boston` has been removed from scikit-learn since version 1.2.

The Boston housing prices dataset has an ethical problem: as
investigated in [1], the authors of this dataset engineered a
non-invertible variable "B" assuming that racial self-segregation had a
positive impact on house prices [2]. Furthermore the goal of the
research that led to the creation of this dataset was to study the
impact of air quality but it did not give adequate demonstration of the
validity of this assumption.z"

sadly i cant agree with others😕 my dumbass have a lot of doubts. what is the purrpose of gridsearch?

Is it still worth watching this video? How much has changed in 2 years? Thank you

Very interesting, Thank you very much

So far into the video, I don't see the data split into train and test samples. Does that mean the model is testing on seen data? If yes, how reliable are these metrics?
Someone shed some light, please.

ok thanks you bro

Just a FYI

`load_boston` has been removed from scikit-learn since version 1.2.

A whole book about racial issues and stuff. Well its not removed but modified. Kaggle has it

Do you guys know where I can download that csv file used in pre-processing part? Thanks!

Great video. Helped me with multiple sections that I had been fumbling my way through. No hard going over some things I already knew aswell.
Thanks for this..👍

Is it just me or is it everyone who thinks that everyone says every language and library is extremely popular and is the main aspect when it comes to building the best things in the world

So amazing. Either this video is especially approachable or I've been exposed to these concepts enough now that they're finally starting to click. Probably both, but the former is definitely a significant factor. Well done

The Boston housing prices dataset has an ethical problem: as
investigated in [1], the authors of this dataset engineered a
non-invertible variable "B" assuming that racial self-segregation had a
positive impact on house prices [2]. Furthermore the goal of the
research that led to the creation of this dataset was to study the
impact of air quality but it did not give adequate demonstration of the
validity of this assumption.

The scikit-learn maintainers therefore strongly discourage the use of
this dataset unless the purpose of the code is to study and educate
about ethical issues in data science and machine learning.

it's great...

What do you mean watch all these videos? Are there different videos series?

truly a great tutorial!

i feel i learned so much, great job sir. Thank you :)

This is an excellent tutorial. Im doing the coursera ibm maachine learning cert and supplementing it with this video. This overall is a much more palatable and easier to understand tutorial of scikit learn and really a machine learning model in general. Awesome work!

>muh data science
Yet you tamper with algorithm 'cause of "racism". Like "This is valid parameter, but I will ignore it and maybe it will go away"

I loved the end chapter that joined machine learning with expert systems I've used 30 years ago...

which interface is this???

very useful... I run the code on idle but it didnt work well, there are something that need to revise like importation of library being after used variable.

Do someone have the credit card fraud .csv similar to the teacher? Because the sheet that I got on Kaggle I can't convert it directly to dataframe (yes, I tried to do some pretreatment on file but in the last row, if I sum up every thing, its returns 0)

this has an awesome didactics

I have one question on time of lapsing GridSearchCV pipeline: how to minimize time of running code, because my model was estimated with mean fit time at least 9 min. My processor is AMD Ryzen 5 5500U with Radeon Graphics 2.10 GHz and 6 cores. Thenk you in advance!

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rr

Very good tutorial.

Sorry but no clear explanation of anything. How would someone know what all these fit, predict functions are doing when you dont even show the dataset??

Is GridSearchCV(... ,cv=3) doing a nested crossvalidation?