Overview
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This course is running for the final time – if you wish to sign up then you must do so by 15 April 2022.
This course will give you an overview of machine learning-based approaches for predictive modelling, including tree-based techniques, support vector machines, and neural networks using Python. These models form the basis of cutting-edge analytics tools that are used for image classification, text and sentiment analysis, and more.
The course contains two case studies: forecasting customer behaviour after a marketing campaign, and flight delay and cancellation predictions.
You will also learn:
- Sampling techniques such as bagging and boosting, which improve robustness and overall predictive power, as well as random forests
- Support vector machines by introducing you to the concept of optimising the separation between classes, before diving into support vector regression
- Neural networks; their topology, the concepts of weights, biases, and kernels, and optimisation techniques
