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Introduction - Machine Learning # 1
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📚About
This is the first lecture of the series entitled “Machine Learning with TensorFlow & Scikit-learn”, we will introduce what Machine Learning is, along with many of its useful applications and use cases. This lecture is outlined as follows:
00:00:00 Introduction
00:00:19 What is Machine Learning ?
00:06:05 Why Machine Learning ?
00:15:07 AlphaZero AI
00:16:24 Types of Machine Learning
00:16:53 Supervised Learning
00:18:39 Unsupervised Learning
00:26:15 Semi-supervised Learning
00:28:14 Reinforcement Learning
00:29:46 epsilon-Learning on the fly
00:30:09 Batch Learning
00:31:36 Online Learning
00:35:04 Instance-based Learning
00:36:05 Model-based Learning
00:36:14 Does Money make people happy ?
00:41:04 What could go wrong ?
00:51:29 Feature Engineering
00:52:33 Overfitting
00:54:04 Sampling Noise
00:56:26 Regularization
01:01:17 Testing & Validating
01:06:41 Outro
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Instructor: Dr. Ahmad Bazzi
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Credits:
Google Photos
TensorFlow
scikit-learn
Numpy
Microsoft OneNote
Python
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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.
[2] Bishop, Christopher M. Pattern recognition and machine learning. springer, 2006.
[3] Friedman, Jerome, Trevor Hastie, and Robert Tibshirani. The elements of statistical learning. Vol. 1. No. 10. New York: Springer series in statistics, 2001.
[4] Burkov, Andriy. The hundred-page machine learning book. Quebec City, Can.: Andriy Burkov, 2019.
[5] Goodfellow, Ian, Yoshua Bengio, and Aaron Courville. Deep learning. MIT press, 2016.
[6] Chollet, Francois. Deep Learning mit Python und Keras: Das Praxis-Handbuch vom Entwickler der Keras-Bibliothek. MITP-Verlags GmbH & Co. KG, 2018.
[7] De Prado, Marcos Lopez. Advances in financial machine learning. John Wiley & Sons, 2018.
[8] Duda, Richard O., Peter E. Hart, and David G. Stork. Pattern classification. John Wiley & Sons, 2012.
[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.
[10] Bonaccorso, Giuseppe. Machine Learning Algorithms: Popular algorithms for data science and machine learning. Packt Publishing Ltd, 2018.
[11] Deisenroth, Marc Peter, A. Aldo Faisal, and Cheng Soon Ong. Mathematics for machine learning. Cambridge University Press, 2020.
[12] Krollner, Bjoern, Bruce J. Vanstone, and Gavin R. Finnie. "Financial time series forecasting with machine learning techniques: a survey." ESANN. 2010.
[13] Singhvi, Surendra. "How to Make Money in Stocks: A Winning System in Good Times or Bad." Management Review 77.9 (1988): 61-63.
[14] Banko, Michele, and Eric Brill. "Scaling to very very large corpora for natural language disambiguation." Proceedings of the 39th annual meeting on association for computational linguistics. Association for Computational Linguistics, 2001.
[15] Wolpert, David H., and William G. Macready. "No free lunch theorems for optimization." IEEE transactions on evolutionary computation 1.1 (1997): 67-82.
#MachineLearning #TensorFlow #MachineLearningTutorial
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