How to program the Best Fit Line - Practical Machine Learning Tutorial with Python p.9

Welcome to the 9th part of our machine learning regression tutorial within our Machine Learning with Python tutorial series. We've been working on calculating the regression, or best-fit, line for a given dataset in Python. Previously, we wrote a function that will gather the slope, and now we need to calculate the y-intercept.

[DarkAwesome]

Boom, regression line.. Yes boss, exactly how I felt. You tutorials are great, thanks a lot for them.

[RahulKumar-oxxd]

Apart from the best understanding u provide, i find your videos most watchable among all crazy AI multiverse...👌👍
Thanks for the efforts back then...

[Opifex_]

I've watched some of your previous tutorials before, and I loved them. You're a great teacher. But now... you're combined something I've been interested in, and have been wanting to learn for a long time with what looks like another great tutorial series! I really hope you will continue making this series, because I will definitely try this one out when I find the time for it!

[evanfreeman]

Thanks sentdex! I'm just starting to explore machine learning and your tutorials are perfect. Really enjoying them

[wanderlust]

just to say that every 6th grader is basically a data scientist/machine learner since one does that stuff in high school :) Seriously, (as you already pointed out) linear regression is not a machine learning technique but a fundamental statistical tool (so are PCA, decision trees etc.) Every scientist uses that stuff every day and might have never heard of machine learning. Great tutorial anyway!!!

[xinningwang]

great videos Harrison! My data science professor recommended your channel to me and this is like the 6th or 7th series I've been watching! Keep up the good work! You are such a great teacher!

[jayshah]

can you please give a tutorial on multi-variable regression and polynomial fir regression

[hanman]

Your really awesome intelligent and Thanks a lot to share knowledge on Machine learning with Python.

[ipekbar]

very good tutorials for learners who does not have enough time

[andychong]

can't wait for the next one, the video is really clear and easy to understand

[arjunyadav]

I really appreciate for the content being taught and the way they are taught.
I think that the coding tutorials(starting ones) would have been better understood if these(ep 7, 8, 9) were taught first.

[sadabwasim]

Wow just wow amazing explanation. Loved it.

[youngzproduction]

I hope you make vids about translating mathematic or statistical equations into python functions. I’m looking forward to seeing it.
Nice vids by the way.

[souviksb]

Hi,
Its a great tutorial. However earlier I came across gradient descent for linear regression. Can you suggest, when to use which one. Can this or something similar be used for multivariate linear regresssion? Or can this be modified for higher degree pollunomial?

[timmayabi]

Thank you very much sir. I can't wait to get to your tutorials on neural networks

[janwillemvanholst]

If you imported numpy, why not use it to the full and have m*xs+b instead of the list comprehension?

[PhantomKenTen]

This might be a strange question, but how would you put a regression line through a stock adjusted close. ie the dates are on the x-axis and the price on the y. This formula wont really work, or am i missing something?

[sushruthprasannakumar]

I did not understand the ""regression_line = [(m*x)+b for x in xs]"" part .
Does it create a list/array of values of y for each value of x?

[ankushbanik]

yes now i understand what is this, love from india

[ShashwatSiddhant]

Hi there sentdex! Awesome tutorial! How can I build a regression model if I have multiple parameters to consider while making a prediction? Say, we have to predict the stock prices and we are consider the day number of the month, month number and weekday. I have also done one-hot encoding on these parameters for better prediction results so the number of columns in my data set for training is 17 excluding the prediction price column. How can I use these parameters to build a regression line? What would be the formula for m? Please point me in the right direction. Thanks!