Support Vector Machine - How Support Vector Machine Works | SVM In Machine Learning | Simplilearn

This Support Vector Machine (SVM) tutorial video will help you understand the basics of the Support Vector Machine algorithm, where and when to use the SVM algorithm, and how Support Vector Machine works. You will learn about hyperplanes and support vectors, how distance margin helps in optimizing the hyperplane, kernel functions in SVM for data transformation and advantages of the SVM algorithm.

Below topics are explained in this Support Vector Machine Tutorial:
00:00 - 01:03 Applications and Agenda
01:04 - 01:58 What is Machine Learning?
01:58 - 03:39 Why support vector machine?
03:39 - 07:59 What is a support vector machine?
07:59 - 09:15 Advantages of support vector machine
09:15 - 26:42 Use Case in Python

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What is a Support Vector Machine?
Support Vector Machines are powerful supervised learning algorithms for both classification and regression. It is a discriminative classifier that is formally defined by a separating hyperplane. So given labelled training data, the algorithm outputs an optimal hyperplane that categorizes new examples.

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[subtlethingsinlife]

Those having problems in application of generating sample blobs, here is the correct syntax and code:
import numpy as np
import matplotlib.pyplot as plt
from sklearn import svm
from sklearn.datasets import make_blobs
X, y = make_blobs(n_samples=40, centers=2, random_state=20)
clf = svm.SVC(kernel='linear', C=1)
clf.fit(X, y)
plt.scatter(X[:, 0], X[:, 1], c=y, s=30, cmap=plt.cm.Paired)
plt.show()

[cheggmi]

I am listening to this again and i must say that even my 5 year old will understand this. You make learning fun. Thank you.

[rahulbatra]

great tutorial! Simple and well explained. Not sure if this has been uploaded but here is the jupyter script:). Although liked typing it out as I'm new to this.


import numpy as np
import matplotlib.pyplot as plt
from sklearn import svm
from import make_blobs
# we create 40 seperable points
X, y = make_blobs(n_samples=40, centers=2, random_state=20)
# fit the model, don't regularize for illustration purposes
clf = svm.SVC(kernel="linear", C=1)
clf.fit(X, y)
# display the data in graph from
plt.scatter(X[:, 0], X[:, 1], c=y, s=30, cmap=plt.cm.Paired)
plt.show()

[bloom]

The best video ever on SVM: learning with fun. You got a great sense of humour and loved your examples 😍😍

[Blawcky]

Honestly I've looked at so many videos on SVM on trying to just understand it and its math, but somehow you've kept it super simple and made me understand it so simply in comparison to all other sources I've seen so far. Thanks so much!

[indrapatel]

Omg these videos are much better then paid courses. Simply superb and thank you sir,

[samratsengupta]

My goodness! is it that simple?
Are you serious?
I have learnt what 2 days of online R&D did not do for me.
I have no doubt that i am at the right place bless this team

[priyanksisodia]

i fall in love with your presentations in all videos

[sharmiislam]

This slide is well informed and nice to have an hands on example in it.

[explorewithjoanne]

I really like the intro part, very clear and easy to understand especially for someone barely has knowledge in machine learning... I'd really appreciate a lot if you can kindly help me with my question. I want to run nonlinear multiple regression based on 1 dependent variable (engagement rate) and 12 independent variables (color of the picture) all measured at continuous level... I wanted to use SVM but now it seems like it only applies if my data has only two classes. Any suggestion from you? Thank you!

[parastooveisi]

I've been looking for these easy to understand explanations for a week. Thanks a lot for this great video!

[voticiano]

Why in the first code example C=1 and the second example C=1000? what is the difference?

[hafzatinnurlatifa]

The explanation is really understandable, thank youu

[KrishnoSarkar]

Good and easy to understand tutorial on SVM. I would also like to see the math behind separating the data. Is SVM separates data into two sets only?

[mruduladdipalli]

First 4 minutes, it cleared all my doubts Thanks alot

[inquisitivelearner]

So, is this one of the classification algorithms?

[ms.mousoomibora]

Hi !! Nice explanation about SVM. Looking forward to hear more use cases.

[VarunSharma-xcfl]

My college teacher studies and teaches from your channel only.

[drpatrickstacey]

excellent! thanks. note, to do the scatter for 3.6+: import matplotlib.pyplot as plt

[faisalrauf]

SIR, your explanation is too good ..can you share the above code of python