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Machine Learning with Python

This project contains course materials presented in IUT - winter 2019

Authors:

Materials

This course is divided into 7 chapters. Each chapter material is in a Jupyter Notebook:

  1. Python and needed python packages for ML
  2. Introduction to ML, Supervised Learning (Regression), Feature Scaling
  3. Supervised Learning (Classification), Model Validation, Outlier Detection
  4. More Supervised Learning (SVM, Decision Tree, Random Forest, ...)
  5. Unsupervised Learning (Clustering) & Dimensionality Reduction
  6. Text Mining
  7. Neural Networks

Question?

Open an issue or contact the authors by:

Acknowledge

Some of the materials of these course inspired from the material of machine learning course Fall 2017

License

This course is licensed under GPLv3.