AI Applications in Data Science and Engineering

This course introduces learners to the core principles of artificial intelligence (AI) and its application in contemporary data science. In just 25 hours of self-paced learning, participants gain an accessible yet solid understanding of how machine learning models are developed, evaluated, and applied in practice.

The course begins by outlining the historical development of AI and the foundational concepts behind machine learning, including supervised and unsupervised learning methods. A special focus is placed on neural networks and the role of deep learning in today’s data-driven technologies. Learners gain insight into model evaluation strategies, including using performance metrics such as accuracy, F1 score, and ROC-AUC.

A key feature of the course is its emphasis on reproducibility, which is essential for scientific credibility and transparency in AI development. Participants are introduced to tools such as Git, GitHub, Jupyter Notebooks, and Google Colab, enabling them to document and replicate their workflows effectively. All practical tasks are conducted using Python and well-established open-source libraries like Scikit-learn, NumPy and TensorFlow.

The course is fully online and self-guided. Learners work through content at their own pace and engage with interactive coding exercises and project-based tasks independently. This flexible structure makes the course particularly suitable for individuals seeking to enhance their skills alongside other academic or professional commitments.