Learning from Data to make Predictions
Learn how to apply statistical learning techniques to real-world business problems in Python by building, interpreting, visualising and evaluating machine learning models to learn from data and make predictions.
This course provides a hands-on and in-depth exploration of the industry-standard Python Scikit-Learn machine learning library with which to build, visualise and evaluate machine learning models applied to real-world business problems and use-cases. This course follows on from our Statistical Learning course, and enables senior data scientists to apply the mathematical techniques introduced in that course to real-world use-cases, from which they can make predictions and derive actionable insights from data. As such, this course details how to build and evaluate linear models for regression and classification, tree-based models, support vector machines, clustering models, manifold learning and applied dimensionality reduction techniques for higher-dimensional problems. This course also details applied techniques for feature selection as well as model selection, visualisation and evaluation techniques.