Predictive Analytics using Machine Learning

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

Week 1: Decision trees
Week 2: Random forests and support vector machines
Week 3: Support vector machines
Week 4: Neural networks
Week 5: Neural network estimation and pitfalls
Week 6: Model comparison