Predictive Analytics using Machine Learning

This course is a part of Predictive Analytics using Python, a 5-course MicroMasters series from edX.

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 you'll 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
• 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