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In this course, you will :
- Learn the distinction between machine learning and other statistical models.
- Build tree-based models, support vector machines, and neural networks to practise.
- In Python, implement the theoretic models in machine learning-based software packages.
- Use machine learning models to solve business problems.
- Random forests, as well as sampling techniques such as bagging and boosting, improve robustness and overall predictive power.
- Before diving into support vector regression, support vector machines will introduce you to the concept of optimising the separation between classes.
- Topology of neural networks, concepts of weights, biases, and kernels, and optimization techniques
Syllabus :
- Decision trees Week
- Random forests and support vector machines Week
- Support vector machines Week
- Neural networks Week
- Neural network estimation and pitfalls Week
- Model comparison
