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