An introduction to machine learning.
Prerequisites
A basic understanding of Python. You will need to know how to write a for loop, if statement, use functions, libraries and perform basic arithmetic. Either of the Software Carpentry Python courses cover sufficient background.
| Setup | Download files required for the lesson | |
| 00:00 | 1. Introduction |
What is machine learning?
What are some useful machine learning techniques? |
| 00:40 | 2. Supervised methods - Regression |
What is supervised learning?
What is regression? How can I model data and make predictions using regression methods? |
| 02:40 | 3. Supervised methods - Classification | How can I classify data into known categories? |
| 03:40 | 4. Ensemble methods |
What are ensemble methods?
What are random forests? How can we stack estimators in sci-kit learn? |
| 05:40 | 5. Unsupervised methods - Clustering |
What is unsupervised learning?
How can we use clustering to find data points with similar attributes? |
| 06:40 | 6. Unsupervised methods - Dimensionality reduction | How do we apply machine learning techniques to data with higher dimensions? |
| 07:40 | 7. Neural Networks |
What are Neural Networks?
How can we classify images using a neural network? |
| 08:30 | 8. Ethics and the Implications of Machine Learning | What are the ethical implications of using machine learning in research? |
| 08:45 | 9. Find out more | Where can you find out more about machine learning? |
| 08:55 | Finish |
The actual schedule may vary slightly depending on the topics and exercises chosen by the instructor.