Building Intelligent Systems: Machine Learning and Generative AI
About This Course
This course covers how to develop and deploy machine learning
models. By the end of the module, learners should have a solid
understanding of machine learning paradigms, training, testing and
deploying machine learning models, and overview of generative AI and
machine learning on unstructured data such as image and text
classification.
What You'll Learn
1. Explain the fundamental concepts of machine learning
paradigms and the data science lifecycle.
2. Apply feature engineering techniques and hyperparameter
tuning to train machine learning models.
3. Evaluate the performance of machine learning models using
appropriate metrics and techniques.
4. Analyze time-series data and implement forecasting algorithms.
5. Create deep learning models for unstructured data.
Entry Requirements
A background in IT is not necessary but preferable. This course is designed to cater to the intricacies of data science, data engineering and AI. Hence, individuals with a background in these disciplines bring valuable prior knowledge that will enhance their learning experience and understanding of the curriculum.
Applicants will be required to undergo a shortlisting process where they must share their resume and complete a pre-course assessment. Our instructors and career consultants will review applicants to assess their suitability before deciding whether to shortlist them for the course.