Machine Learning with Python

This project contains course materials presented in IUT - Fall 2017

Authors:

• Alireza Omidi
• Mohammad Sadegh Akhondzadeh

Materials

This course is divided into 9 chapters. Each chapter material is in a Jupyter Notebook:

1. Introduction - [Notebook] [HTML]
2. Supervised Learning: Regression - [Notebook] [HTML]
3. Supervised Learning: Classification - [Notebook] [HTML]
4. Supervised Learning: A bit more - [Notebook] [HTML]
5. Model Validation, Feature Scaling & Outlier Detection - [Notebook] [HTML]
6. Unsupervised Learning: Clustering - [Notebook] [HTML]
7. PCA & Feature Selection - [Notebook] [HTML]
8. Text Mining - [Notebook] [HTML]
9. Neural Networks & Deep Learning - [Notebook] [HTML]

Question?

Open an issue or contact the authors by:

• [email protected] or
• [email protected]

License

This course is licensed under GPLv3.