Overview
This course will introduce participants to machine learning, focusing more on the practical and applied aspects rather than theory. The course will discuss machine learning concepts, and briefly introduce Python, PyCharm environment, Scikit-learn, Numpy, Anaconda, and Keras toolkits.
Regression as a basic machine learning method will be discussed and practised. Different models and examples of regression will be reviewed. Support Vector Machines (SVM) along with their applications in function estimation and classification will also be introduced. We will also discuss artificial neural networks and introduce deep learning.
Participants will learn how to implement machine learning to solve real-life problems more productively and efficiently.
Course Description & Learning Outcomes
At the end of this course, participants will be able to:
• Understand the way regression, support vector machines (SVM), and artificial neural networks (ANN) work
• Recognise the applications, advantages and disadvantages of regression, SVM, and ANN methods
• Design and implement basic regression, SVM-based, and ANN-based algorithms in clustering, classification, and function estimation applications
Schedule
End Date: 11 Feb 2025, Tuesday
Location: Online
Pricing
Skills Covered
PROFICIENCY LEVEL GUIDE
Beginner: Introduce the subject matter without the need to have any prerequisites.
Proficient: Requires learners to have prior knowledge of the subject.
Expert: Involves advanced and more complex understanding of the subject.
- Machine Learning (Proficiency level: Beginner)
