Applied Machine Learning

Overview

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.

Course Details

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.

Course Modules

• 1. Introduction to Machine Learning
• 2. Scikit-Learn Basics
• 3. Linear Models - Regression Part 1
• 4. Linear Models - Regression Part 2
• 5. Linear Models - Classification Part 1
• 6. Linear Models - Classification Part 2
• 7. Tree-Based Models Part 1
• 8. Tree-Based Models Part 2
• 9. Support Vector Machines Part 1
• 10. Support Vector Machines Part 2
• 11. Clustering Models Part 1
• 12. Clustering Models Part 2
• 13. Manifold Learning Part 1
• 14. Manifold Learning Part 2
• 15. Dimensionality Reduction Part 1
• 16. Dimensionality Reduction Part 2
• 17. Feature Selection
• 18. Model Selection and Evaluation

Requirements

• Introduction to Python or equivalent.
• Linear Algebra or equivalent.
• Statistical Learning or equivalent.

Outcomes

• The ability to apply statistical learning techniques in Python.
• The ability to build, interpret, visualise and evaluate supervised and unsupervised machine learning models applied to real-world business problems and use-cases.
• The ability to select features and models based on the specific context of the business problem.
• Advanced knowledge of the industry-standard Python Scikit-Learn machine learning library.