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Setup |
Download files required for the lesson |
00:00 |
1. Introduction
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What is machine learning?
What are some useful machine learning techniques?
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00:40 |
2. Supervised methods - Regression
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What is supervised learning?
What is regression?
How can I model data and make predictions using regression methods?
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02:40 |
3. Supervised methods - Classification
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How can I classify data into known categories?
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03:40 |
4. Ensemble methods
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What are ensemble methods?
What are random forests?
How can we stack estimators in sci-kit learn?
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05:40 |
5. Unsupervised methods - Clustering
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What is unsupervised learning?
How can we use clustering to find data points with similar attributes?
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06:40 |
6. Unsupervised methods - Dimensionality reduction
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How do we apply machine learning techniques to data with higher dimensions?
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07:40 |
7. Neural Networks
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What are Neural Networks?
How can we classify images using a neural network?
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08:30 |
8. Ethics and the Implications of Machine Learning
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What are the ethical implications of using machine learning in research?
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08:45 |
9. Find out more
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Where can you find out more about machine learning?
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08:55 |
Finish |
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