Mastering Support Vector Machines with Python and Scikit-Learn

Are you ready to delve into the world of machine learning algorithms and enhance your understanding of SVM? In this comprehensive tutorial, we'll guide you through the ins and outs of Support Vector Machines, one of the most powerful and versatile tools in the machine learning toolbox.
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[@RyanAndMattDataScience]

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[@pmnt98]

mean1 = 55
std_dev1 = 10
num_samples = 500
column1_numbers = np.random.normal(mean1, std_dev1, num_samples)
column1_numbers = np.clip(column1_numbers, 30, 120)
column1_numbers = np.round(column1_numbers).astype(int)
mean2 = 18
std_dev2 = 3
column2_numbers = np.random.normal(mean2, std_dev2, num_samples)
column2_numbers = np.clip(column2_numbers, 12, 26)
column2_numbers = np.round(column2_numbers).astype(int)
column3_numbers = np.random.randint(2, size=num_samples)
column3_numbers[column1_numbers > mean1] =1
data = {"Miles_Per_week": column1_numbers,
"Farthest_run": column2_numbers,
"Qualified_Boston_Marathon": column3_numbers}
df = pd.DataFrame(data)

[@pymav]

Hello Ryan. Thanks for the video. I can’t seem to find where you put down the code for the “data generation” and matplotlib. Could you help with that. Thanks

[@rahulx21]

very useful

[@RyanAndMattDataScience]

Thanks

[@misterx3321]

Thanks for the video, could you by any chance cover glms or bayesian linear regression?

[@RyanAndMattDataScience]

Yes in the future. Have about a months worth of videos currently being built for the scikit course I’ve been working on

[@henry-o8i]

Hey wonder if you have the code for the data so we can follow along?

[@RyanAndMattDataScience]

I don’t have the code atm, but I plan on uploading all the videos code to my website in the near future