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.
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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
This course is part of the Predictive Analytics using Python MicroMasters Program.
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
Syllabus
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
Prerequisites
You should be familiar with an undergraduate level, or have a background, in mathematics and statistics. Previous experience with a procedural programming language is beneficial (e.g. Python, C, Java, Visual Basic).
Learners pursuing the MicroMastersprogramme are strongly recommended to complete PA1.1x Introduction to Predictive Analytics using Python and PA1.2x Successfully Evaluating Predictive Modelling and PA1.3x Statistical Predictive Modelling and Applications on the verified track prior to undertaking this course.
