LASSO Regression & Elastic-Net Regression | Machine Learning #11
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This lecture talks about the LASSO Regression. LASSO stands for least absolute shrinkage and selection operator. It is a regression analysis method that performs both regularization and variable selection in order to improve the prediction accuracy and interpretability of the statistical model it produces. It was originally introduced in geophysics literature in 1986, and later independently rediscovered and popularized in 1996 by Robert Tibshirani. On the other hand, Elastic-Net combines both LASSO and Ridge through an appropriate linear combination.
⏲Outline⏲
00:00 Introduction
00:07 LASSO Regression
01:10 LASSO Regression on Python
04:37 Elastic-Net Regression
08:50 Elastic-Net Regression on Python
09:49 Early Stopping
22:37 Outro
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Lecture 1: Introduction • Introduction - Machine Learning # 1
Lecture 2: Binary Classification & SGD Classifier • Stochastic Gradient Descent Classifie...
Lecture 3: Performance Measures • Performance Measures - Machine Learni...
Lecture 4: Multiclass classification & Cross Validation • Multiclass classification & Cross Val...
Lecture 5: Gradient Descent • Gradient Descent - Machine Learning # 5
Lecture 6: Multilabel and Multioutput Classification • Multilabel and Multioutput Classifica...
Lecture 7: Linear Regression with Louis from "What is Artificial Intelligence" • Linear Regression | Machine Learning # 7
Lecture 8: Polynomial Regression feat. Luis Serrano & YouTube's Video Recommendation Algorithm • Polynomial Regression w Luis Serrano ...
Lecture 9: Simulated Annealing x SGD x Mini-batch • Simulated Annealing x SGD x Mini-batc...
Lecture 10: Ridge Regression • Ridge Regression | Tikhonov Regulariz...
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Instructor: Dr. Ahmad Bazzi
IG: / 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?wdorig...
References:
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https://www.amazon.com/Hands-Machine-...
[2] Bishop, Christopher M. Pattern recognition and machine learning. springer, 2006.
https://www.amazon.com/Pattern-Recogn...
[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-Stati...
[4] Burkov, Andriy. The hundred-page machine learning book. Quebec City, Can.: Andriy Burkov, 2019.
https://www.amazon.com/Hundred-Page-M...
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https://www.amazon.com/Deep-Learning-...
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https://www.amazon.com/Deep-Learning-...
[7] De Prado, Marcos Lopez. Advances in financial machine learning. John Wiley & Sons, 2018.
https://www.amazon.com/Advances-Finan...
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https://www.amazon.com/Pattern-Classi...
[9] Lapan, Maxim. Deep Reinforcement Learning Hands-On: Apply modern RL methods, with deep Q-networks, value iteration, policy gradients, TRPO, AlphaGo Zero and more. Packt Publishing Ltd, 2018.
https://www.amazon.com/Deep-Reinforce...
[10] Bonaccorso, Giuseppe. Machine Learning Algorithms: Popular algorithms for data science and machine learning. Packt Publishing Ltd, 2018.
https://www.amazon.com/Machine-Learni...
[11] Deisenroth, Marc Peter, A. Aldo Faisal, and Cheng Soon Ong. Mathematics for machine learning. Cambridge University Press, 2020.
https://mml-book.github.io/book/mml-b...
[12] Krollner, Bjoern, Bruce J. Vanstone, and Gavin R. Finnie. "Financial time series forecasting with machine learning techniques: a survey." ESANN. 2010.
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