In this advanced course, participants will learn specific concepts and advanced techniques of machine learning, such as Support Vector Machines, Random Forest, Boosting and Stacking, Cross Validation, Pipeline and many more.
Upon completing the course, participants will be able to explain the essential principles of algorithms used for advanced machine learning. The course also provides participants with ample hands-on experience in building, validating and improving advanced machine learning models using Python.
We recommend participants to complete Machine Learning Fundamentals before signing up for this course.
Laptops will be provided for the duration of the course.
Learning Objectives
The key topics covered in this module include:
- Advanced machine learning algorithms (e.g. Random Forest, SVM, Naïve Bayes)
- Model Improvement techniques
- Pipeline and Dimensionality Reduction
Who should attend?
Artificial Intelligence, Machine Learning Engineer
Entry Requirement
Basic knowledge in Python programming is required. Basic knowledge of calculus would be an advantage.
Certification
Participants will be awarded a certificate of completion upon meeting the 75% course attendance requirement.
For courses with assessment component, participants will be awarded the certification of completion upon passing the assessment. Otherwise, a certification of attendance will be issued instead upon meeting the 75% course attendance requirement.