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Hands-on scikit-learn: build, tune and judge real models in Python.
| Week | Lesson | Shows up in | Real practice |
|---|---|---|---|
| 1 | Your first end-to-end ML project End-to-end machine learning project on scikit-learn | Quiz 1 | 60 real questions |
| 2 | The project continued: pipelines and tuning End-to-end machine learning project on scikit-learn (Continued) | Quiz 1 | 40 real questions |
| 3 | Linear regression and gradient descent in sklearn Regression on scikit-learn - Linear regression, Gradient descent - batch and stochastic | Quiz 1 | 60 real questions |
| 4 | Polynomial regression and regularisation Polynomial regression, Regularized models | Quiz 1 | 60 real questions |
| 5 | Logistic regression in sklearn Logistic regression | Quiz 2 | 28 real questions |
| 6 | Binary classifiers and how to judge them Classification on scikit-learn - Binary classifier | Quiz 2 | 60 real questions |
| 7 | Multiclass classifiers Classification on scikit-learn - Multiclass classifier | Quiz 2 | 9 real questions |
| 8 | SVMs in sklearn Support Vector Machines using scikit-learn | Quiz 2 | 22 real questions |
| 9 | Decision trees and ensembles Decision Trees, Ensemble Learning and Random Forests | End Term | 53 real questions |
| 10 | Random forests, boosting and tuning Decision Trees, Ensemble Learning and Random Forests (Continued) | End Term | 6 real questions |
| 11 | Neural networks in sklearn Neural networks models in scikit-learn | End Term | 14 real questions |
| 12 | Unsupervised learning in sklearn Unsupervised learning | End Term | 58 real questions |
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