Note:
These are notes taken from (Géron, 2023).
Notes
- HML_001 The Machine Learning Landscape
- HML_002 End-to-End Machine Learning Project
- HML_003 Classification
- HML_004 Training Models
- HML_005 Support Vector Machines
- HML_006 Decision Trees
- HML_007 Ensemble Learning and Random Forests
- HML_008 Dimensionality Reduction
- HML_009 Unsupervised Learning Techniques
- HML_010 Introduction to Artificial Neural Networks with Keras
Bibliography
- Géron, A., 2023. Hands-on machine learning with Scikit-Learn, Keras, and TensorFlow: concepts, tools, and techniques to build intelligent systems, Third edition. ed, Data science / machine learning. O’Reilly, Beijing Boston Farnham Sebastopol Tokyo.
- Banko, M., Brill, E., 2001. Scaling to very very large corpora for natural language disambiguation, in: Proceedings of the 39th Annual Meeting on Association for Computational Linguistics - ACL ’01. Presented at the the 39th Annual Meeting, Association for Computational Linguistics, Toulouse, France, pp. 26–33. https://doi.org/10.3115/1073012.1073017
- Halevy, A., Norvig, P., Pereira, F., 2009. The Unreasonable Effectiveness of Data. IEEE Intell. Syst. 24, 8–12. https://doi.org/10.1109/MIS.2009.36
- Correlation, 2024. . Wikipedia.
- McCulloch, W.S., Pitts, W., 1943. A logical calculus of the ideas immanent in nervous activity. Bulletin of Mathematical Biophysics 5, 115–133. https://doi.org/10.1007/BF02478259
- Hebb, D. O. The organization of behavior: A neuropsychological theory. New York: John Wiley and Sons, Inc., 1949. 335 p. $4.00, 1950. . Science Education 34, 336–337. https://doi.org/10.1002/sce.37303405110
- Rumelhart, D.E., Hinton, G.E., Williams, R.J., 1988. Learning Internal Representations by Error Propagation, in: Readings in Cognitive Science. Elsevier, pp. 399–421. https://doi.org/10.1016/B978-1-4832-1446-7.50035-2