Learn how to build predictive models using machine learning.
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
What will you learn
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