Linear Regression | Machine Learning # 7

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📚About
This lecture talks simply talks about Linear Regression. The lecture also shows how to get the job done on Python and with the help of sklearn. We've got @WhatsAI as a host on this lecture. Louis of @WhatsAI helps me explain what Linear Regression is. Please help me in welcoming Louis.

⏲Outline⏲
00:00 Introduction
01:30 What is Linear Regression ?
04:26 GDP vs Life Satisfaction Example
09:15 Features & Model Parameters
12:23 How do we train it ?
16:28 Python: The manual way
20:28 Python: The sklearn way
21:46 Computational Complexity
26:12 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
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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

[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-Reinforcement-Learning-Hands-Q-networks-ebook/dp/B076H9VQH6

[10] Bonaccorso, Giuseppe. Machine Learning Algorithms: Popular algorithms for data science and machine learning. Packt Publishing Ltd, 2018.
https://www.amazon.com/Machine-Learning-Algorithms-reference-algorithms-ebook/dp/B072QBG11J

[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-book.pdf

[12] Krollner, Bjoern, Bruce J. Vanstone, and Gavin R. Finnie. "Financial time series forecasting with machine learning techniques: a survey." ESANN. 2010.

#MachineLearning #TensorFlow #MachineLearningTutorial

Видео Linear Regression | Machine Learning # 7 канала Ahmad Bazzi