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1 | # Machine Learning |
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| 3 | Foundations of machine learning: how to go from raw data to a trained, evaluated model. |
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5 | **Prerequisites:** basic Python and the [Mathematics](/en/Mathematics) course (linear algebra, probability, statistics). |
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| 7 | ## Syllabus |
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| 9 | 1. [Introduction](/en/Machine%20Learning/01%20Introduction) |
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| 10 | 2. [General concepts](/en/Machine%20Learning/02%20General%20concepts) |
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11 | 3. [Probabilistic formulation](/en/Machine%20Learning/03%20Probabilistic%20formulation) |
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| 12 | 4. [Linear regression](/en/Machine%20Learning/04%20Linear%20regression) |
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| 13 | 5. [Linear classification](/en/Machine%20Learning/05%20Linear%20classification) |
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| 14 | 6. [Multilayer neural networks](/en/Machine%20Learning/06%20Multilayer%20neural%20networks) |
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15 | 7. [Decision trees and ensemble methods](/en/Machine%20Learning/07%20Decision%20trees%20and%20ensemble%20methods) |
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| 17 | --- |
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18 | [MLOps](/en/MLOps) · [Home](/en) |
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