Machine Learning Tutorial Python – 10 Support Vector Machine (SVM)
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Support vector machine (SVM) is a popular classification algorithm. This tutorial covers some theory first and then goes over python coding to solve iris flower classification problem using svm and sklearn library. We also cover different parameters such as gamma, regularization and how to fine tune svm classifier using these parameters. Basically the way support vector machine works is it draws a hyper plane in n dimension space such that it maximizes the margin between classification groups.
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Exercise: Open above notebook from github and go to the end.
Exercise solution: https://github.com/codebasics/py/blob/master/ML/10_svm/Exercise/10_svm_exercise_digits.ipynb
Topics that are covered in this Video:
0:20 Theory (Explain support vector machine using sklearn iris dataset flower classification problem)
3:11 What is Gamma?
4:21 What is Regularization?
6:32 Coding (Start)
18:08 sklearn.svm SVC
21:41 Exercise (Classify hand written digits dataset from sklearn using SVM)
Machine Learning Tutorial Python 11 Random Forest: https://www.youtube.com/watch?v=ok2s1vV9XW0\u0026list=PLeo1K3hjS3uvCeTYTeyfe0rN5r8zn9rw\u0026index=12
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Jupyter Notebook: https://www.youtube.com/watch?v=q_BzsPxwLOE\u0026list=PLeo1K3hjS3uuZPwzACannnFSn9qHn8to8
Tools and Libraries:
Scikit learn tutorials
Machine learning with scikit learn tutorials
Machine learning with sklearn tutorials
To download csv and code for all tutorials: go to https://github.com/codebasics/py, click on a green button to clone or download the entire repository and then go to relevant folder to get access to that specific file.
Support Vector Machines Part 1 (of 3): Main Ideas!!!
Support Vector Machines are one of the most mysterious methods in Machine Learning. This StatQuest sweeps away the mystery to let know how they work.
Part 2: The Polynomial Kernel: https://youtu.be/Toet3EiSFcM
Part 3: The Radial (RBF) Kernel: https://youtu.be/Qc5IyLW_hns
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NOTE: This StatQuest assumes you already know about…
The bias/variance tradeoff: https://youtu.be/EuBBz3bIaA
Cross Validation: https://youtu.be/fSytzGwwBVw
ALSO NOTE: This StatQuest is based on description of Support Vector Machines, and associated concepts, found on pages 337 to 354 of the Introduction to Statistical Learning in R: http://faculty.marshall.usc.edu/garethjames/ISL/
I also found this blogpost helpful for understanding the Kernel Trick: https://blog.statsbot.co/supportvectormachinestutorialc1618e635e93
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0:00 Awesome song and introduction
0:40 Basic concepts and Maximal Margin Classifiers
4:35 Soft Margins (allowing misclassifications)
6:46 Soft Margin and Support Vector Classifiers
12:23 Intuition behind Support Vector Machines
15:25 The polynomial kernel function
17:30 The radial basis function (RBF) kernel
18:32 The kernel trick
19:31 Summary of concepts
Support Vector Machine In Python | Machine Learning in Python Tutorial | Python Training | Edureka
Python Certification Training: https://www.edureka.co/machinelearningcertificationtraining
This Edureka video on ‘Support Vector Machine In Python’ covers A brief introduction to Support Vector Machine in Python with a use case to implement SVM using Python. Following are the topics discussed:
Introduction To Machine learning
What is Support Vector Machine?
How Does SVM Work?
SVM Use Cases
How To Implement SVM?
Character Recognition Using SVM
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Machine Learning Lecture 14 \”(Linear) Support Vector Machines\” -Cornell CS4780 SP17
machine along vector in artcam
[V bit carving] woks only on closed vectors.
If you have only center line of the stroke (open vectors). You can use [machine along vector] function to do similar work.
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