In the past decade, we have observed the expeditious evolution and tremendous applications of machine learning, such as unmanned vehicles, autonomous language translation, and smart healthcare. This course will introduce the fundamental knowledge of machine learning techniques via a series of hands-on real-world examples in Python. The overall aim is to provide the students with a good understanding of machine-learning technologies, building machine learning with Python, and applying machine-learning technologies to address real-world problems. In the course projects, students will also have an opportunity to explore cutting-edge machine-learning technologies and develop their own machine-learning-based solutions.

Learning Outcomes
 At the end of the course, you should be able to:
 • Explain the relationship (main mechanisms, internal logic, computing components, and the usage constraints) of 8 machine learning models (Linear Regression, Logistic Regression, Fully-Connected Neural Network (FCNN), Convolutional Neural Network (CNN), Recurrent Neural Network (RNN), Autoencoder, General Adversarial Network (GAN), and Reinforcement Learning (RL)).
• Program the basic realization of the machine learning models, stated in Learning Objective 1, in Python.
• Apply the eight machine learning models stated in Learning Objective 1 to solve real-world problems.
 

 

*This course is a joint collaboration between UCSI and Purdue University. Upon completion, participants will receive a certificate co-issued by both UCSI and Purdue University.

Start Date : 01 May 2025 End Date : 31 Jul 2025 Price : RM 3000.00 Per Course
Number of Item : 1
Total (RM) : 3000.00