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
In this course, you will:
- Understand the difference between machine learning and other statistical models
- Practice building tree-based models, support vector machines and neural networks
- Implement the theoretic models in machine learning-based software packages in Python
- Apply machine learning models to business situations
*Program faculty is subject to change
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
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Last updated December 24, 2020
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