Soft & Hard Margin Support Vector Machine (SVM)| Machine Learning with TensorFlow & scikit-learn #13

The first 1000 people to use the link will get a free trial of Skillshare Premium Membership: https://skl.sh/ahmadbazzi01211 📚About This lecture focuses on the theoretical as well as practical aspects of the Support Vector Machines. It is a supervised learning…

Soft & Hard Margin Support Vector Machine (SVM)| Machine Learning with TensorFlow & scikit-learn #13

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The first 1000 people to use the link will get a free trial of Skillshare Premium Membership: https://skl.sh/ahmadbazzi01211

📚About
This lecture focuses on the theoretical as well as practical aspects of the Support Vector Machines. It is a supervised learning model associated with learning algorithms that analyze data used for classification and regression analysis. Developed at AT&T Bell Laboratories by Vapnik with colleagues (Boser et al., 1992, Guyon et al., 1993, Vapnik et al., 1997), it presents one of the most robust prediction methods, based on the statistical learning framework or VC theory proposed by Vapnik and Chervonenkis (1974) and Vapnik (1982, 1995).

⏲Outline⏲
00:00:00 Introduction
00:01:11 Support Vector Machines
00:03:55 Supporting Vectors and Hyperplanes
00:07:05 SVM Mathematical Modelling
00:08:58 Hard Margin SVM
00:47:21 Outlier Sensitivity & Linear Separability
00:49:11 Hard Margin SVM on Python
01:13:15 Soft Margin SVM
01:27:09 Soft Margin SVM on Python
01:31:47 Outro

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Lecture 1: Introduction https://youtu.be/yeTAlrhdzhc
Lecture 2: Binary Classification & SGD Classifier https://youtu.be/aXpsCyXXMJE
Lecture 3: Performance Measures https://youtu.be/UA_ZAwPVLxg
Lecture 4: Multiclass classification & Cross Validation https://youtu.be/5KyH6v8oKNQ
Lecture 5: Gradient Descent https://youtu.be/OWM0wMtUhME
Lecture 6: Multilabel and Multioutput Classification https://youtu.be/bDdjebakjbA
Lecture 7: Linear Regression with Louis from “What is Artificial Intelligence” https://youtu.be/JWQJMoDC9hg
Lecture 8: Polynomial Regression feat. Luis Serrano & YouTube’s Video Recommendation Algorithm https://youtu.be/HmmkA-EFaW0
Lecture 9: Simulated Annealing x SGD x Mini-batch https://youtu.be/3xJ4-2LUiHU
Lecture 10: Ridge Regression https://youtu.be/PtBuqAdbpfY
Lecture 11: LASSO Regression and Elastic-Net Regression https://youtu.be/kNiYiUiW8dY
Lecture 12: Logistic Regression & SoftMax Regression https://youtu.be/JJFT5kLBUjg

SVM Convex Optimization Application: https://youtu.be/1yYhSA8TzcA
Quadratic Programming: https://youtu.be/kM52hSvBY4k
KKT conditions: https://youtu.be/JFbC2uoN7e4
Complementary slackness: https://youtu.be/tFWjzEQMq2g
Lagrange Dual Function: https://youtu.be/Qneah_lyQ0o
Lagrange Dual Problem: https://youtu.be/0WpYucMfaHM

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Instructor: Dr. Ahmad Bazzi
IG: https://www.instagram.com/drahmadbazzi/
Browser: https://www.google.com/chrome/

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Credits:

Google
https://www.google.com/

Google Photos
https://www.google.com/photos/about/

TensorFlow
https://www.tensorflow.org/

scikit-learn
https://scikit-learn.org/stable/

Numpy
https://numpy.org/

Microsoft OneNote
https://www.onenote.com/signin?wdorigin=ondc

Python
https://www.python.org/

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References:
[1] Géron, Aurélien. Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems. O’Reilly Media, 2019.
https://www.amazon.com/Hands-Machine-Learning-Scikit-Learn-TensorFlow/dp/1492032646

[2] Bishop, Christopher M. Pattern recognition and machine learning. springer, 2006.
https://www.amazon.com/Pattern-Recognition-Learning-Information-Statistics/dp/0387310738

[3] Friedman, Jerome, Trevor Hastie, and Robert Tibshirani. The elements of statistical learning. Vol. 1. No. 10. New York: Springer series in statistics, 2001.
https://www.amazon.com/Elements-Statistical-Learning-Prediction-Statistics/dp/0387848576

[4] Burkov, Andriy. The hundred-page machine learning book. Quebec City, Can.: Andriy Burkov, 2019.
https://www.amazon.com/Hundred-Page-Machine-Learning-Book-ebook/dp/B07MGCNKXB

[5] Goodfellow, Ian, Yoshua Bengio, and Aaron Courville. Deep learning. MIT press, 2016.
https://www.amazon.com/Deep-Learning-Adaptive-Computation-Machine/dp/0262035618

[6] Chollet, Francois. Deep Learning mit Python und Keras: Das Praxis-Handbuch vom Entwickler der Keras-Bibliothek. MITP-Verlags GmbH & Co. KG, 2018.
https://www.amazon.com/Deep-Learning-Python-Francois-Chollet/dp/1617294438

[7] De Prado, Marcos Lopez. Advances in financial machine learning. John Wiley & Sons, 2018.
https://www.amazon.com/Advances-Financial-Machine-Learning-Marcos/dp/1119482089

[8] Duda, Richard O., Peter E. Hart, and David G. Stork. Pattern classification. John Wiley & Sons, 2012.
https://www.amazon.com/Pattern-Classification-Pt-1-Richard-Duda/dp/0471056693

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