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? |
| 00:40 | 2. Regression |
How can I make linear regression models from data?
How can I use logarithmic regression to work with non-linear data? |
| 01:55 | 3. Introducing Scikit Learn | How can I use scikit-learn to process data? |
| 02:30 | 4. Clustering with Scikit Learn | How can we use clustering to find data points with similar attributes? |
| 03:05 | 5. Dimensionality Reduction | How can we perform unsupervised learning with dimensionality reduction techniques such as Principle Component Analysis (PCA) and t-distributed Stochastic Neighbor Embedding (t-SNE)? |
| 03:05 | 6. Neural Networks | How can we classify images using a neural network? |
| 03:55 | 7. Ethics and Implications of Machine Learning | What are the ethical implications of using machine learning in research? |
| 04:10 | 8. Find out more | Where can you find out more about machine learning? |
| 04:20 | Finish |
The actual schedule may vary slightly depending on the topics and exercises chosen by the instructor.