Mastering Support Vector Machines with Python and Scikit-Learn

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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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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 =

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 =

column3_numbers = np.random.randint(2, size=num_samples)
> mean1] =1

data = {"Miles_Per_week": column1_numbers,
"Farthest_run": column2_numbers,
"Qualified_Boston_Marathon": column3_numbers}

df = pd.DataFrame(data)

pmnt

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

pymav

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

misterx

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

henry-oi

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