Machine Learning Using Python
MEAFA Professional Development Workshop
- Setting up Python for the workshop
- Recommended reading
- Workshop resources
- Directions for further study
Setting up Python for the workshop
Instructions for setting up a Python environment. Even though computers will be provided, you are highly encouraged to use your own laptop so that you are able to immediately continue working with these tools upon the conclusion of the workshop. We will provide assistance for the installation in the first day of the workshop, if you require it.
Installing additional Python packages. The workshop will rely on the additional machine learning and data visualisation packages listed here.
A Few Useful Things to Know About Machine Learning (Pedro Domingos). An overview of the essential lessons from applied machine learning. We will explore these concepts extensively in the workshop.
Hands-On Machine Learning with Scikit-Learn and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems (Aurélien Géron). My recommendation for those who would like to have a book reference for the topics covered in the workshop.
Lesson 1: Introduction to Machine Learning.
Lesson 2: Regularised Linear Models
Lesson 3: Naive Bayes.
Lesson 4: Logistic Regression and Optimal Decisions.
Lesson 5: Decision Trees and Random Forests.
Lesson 6: Boosting.
Suggested reading: Introduction to Boosted Trees (from the XGBoost documentation).
Lesson 7: Ensemble Learning and Model Stacking.
Lesson 8: Application to a Kaggle Regression Competition.
Lesson 9: Support Vector Machines
Lesson 10: Neural Networks.
The lesson notes draw material from the following references, including some figures.
The Elements of Statistical Learning by Trevor Hastie and Robert Tibshirani.
An Introduction to Statistical Learning by Gareth James, Daniela Witten, Trevor Hastie, and Robert Tibshirani.
Machine Learning: A Probabilistic Perspective by Kevin P. Murphy.