Introduction - Machine Learning # 1

☕️ Buy me a coffee: https://paypal.me/donationlink240 🙏🏻 Support me on Patreon:   / ahmadbazzi   📚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 ============================================================ Instructor: Dr. Ahmad Bazzi IG:   / drahmadbazzi   Browser: https://www.google.com/chrome/ ============================================================ 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... Python https://www.python.org/ ============================================================ 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-... [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... [5] Goodfellow, Ian, Yoshua Bengio, and Aaron Courville. Deep learning. MIT press, 2016. https://www.amazon.com/Deep-Learning-... [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-... [7] De Prado, Marcos Lopez. Advances in financial machine learning. John Wiley & Sons, 2018. https://www.amazon.com/Advances-Finan... [8] Duda, Richard O., Peter E. Hart, and David G. Stork. Pattern classification. John Wiley & Sons, 2012. 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. [13] Singhvi, Surendra. "How to Make Money in Stocks: A Winning System in Good Times or Bad." Management Review 77.9 (1988): 61-63. https://www.amazon.com/How-Make-Money... [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