Machine Learning

About This Course
Learners will be able to:
• Evaluate prospective analytical tools and platforms for their functional capabilities and ability to meet requirements of the analytic environment
• Develop new algorithms to enable the learning, improvement, adaptation or reproduction of outcomes
• Develop regression models, including linear, multiple and logistic regression models
• Develop mathematical models to isolate trends and optimise data-driven decision making
• Create learning models with a discrete set of environment states, actions and reinforcement signals
• Develop testing procedures to evaluate the data model
• Analyse root causes of any issues highlighted
• Facilitate changes to statistical models, to optimise performance and yield intended outcomes
• Apply complex and advanced statistical analysis and modelling techniques
• Uncover underlying relationships among different variables

What You’ll Learn
This unit covers the basics of Machine Learning, starting with an introduction to the field. The unit discusses Supervised Learning techniques such as Linear Regression, Logistic Regression, Decision Trees, Random Forest, Naive Bayes, and K-Nearest Neighbors. It also covers Unsupervised Learning techniques such as Clustering and Dimensionality Reduction. In addition, it teaches Ensemble Learning, Deep Learning, and Model Evaluation and Selection.
By the end of the course, learners will have a solid foundation in the different Machine Learning techniques and be able to apply them in real-world scenarios.