Customising your models with TensorFlow 2

Imperial College London is a world-leading science and engineering university located in the heart of London. Founded in 1907, it is renowned for excellence in research, innovation and teaching across STEM fields. With over 20,000 students, including more than 8,300 international students, Imperial offers a global, diverse community and strong industry connections that enhance learning and employability.

Imperial’s academic portfolio spans research-led undergraduate and postgraduate programs across science, engineering, medicine and business, with some 102 programs available to students. The college’s reputation is reflected in exceptionally high ratings for teaching and classes, modern facilities and strong student satisfaction. Research centres and partnerships with industry provide practical experience, internships and access to cutting-edge laboratories that prepare graduates for leadership roles.

Life at Imperial combines intense academic focus with the cultural and professional opportunities of London. Campus facilities, accommodation options and student services support wellbeing and career development, while extracurricular clubs and societies foster community and leadership. For international students seeking rigorous STEM training, Imperial delivers world-class instruction, extensive research opportunities and a powerful alumni network that supports global careers.

This program is for students who want to learn about customising models with TensorFlow 2, offered as a non-degree course in English at Imperial College London, lasting several weeks and available online.

The curriculum covers specific skills in machine learning and deep learning, including how to build and customise models using TensorFlow 2, with hands-on components to help students practice their skills.

After completing this program, students can pursue careers as Machine Learning Engineers, Data Scientists, AI Researchers, Software Developers, or Data Analysts, and can work in various industries such as technology, finance, or healthcare.