Author: Ankush Agarwal, University of Glasgow
This introductory course consists of ten units. Each unit corresponds to interactive Jupyter notebooks, which are also available as a static PDF file.
The lecture content is based on the book Machine Learning with Pytorch and Scikit-learn by Sebastian Raschka @rasbt.
| Unit | Title | |
|---|---|---|
| 1 | Basics of machine learning classifiers | |
| 2 | Machine learning classifiers using scikit-learn | |
| 3 | Multi-layer Artificial Neural Network | |
| 4 | Introduction to PyTorch for Neural Networks | |
| 5 | Recurrent Neural Networks for Modeling Sequential Data | |
| 6 | Self-Attention in Transformers | |
| 7 | Transformers in Practice | PDF PDF |
| 8 | Generative Adversarial Networks for Synthetic Data | PDF PDF |
| 9 | Decision Trees and Random Forests | |
| 10 | Ensemble Learning |
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