Join a booming, in-demand field with a Master’s degree in Machine Learning from one of the top universities in the world. In this program, you will develop an in-depth understanding of machine learning models, alongside invaluable practical skills and guided experience in applying them to real-world problems. The curriculum is designed to propel your engineering or data science career forward, allowing you to choose the path that’s right for you, be that a role as a data scientist, a machine learning engineer, or a computational statistician. With hands-on projects, you’ll build a portfolio to showcase your new skills in everything from probabilistic modeling, deep learning, unstructured data processing and anomaly detection. You will build a strong foundation in mathematics and statistics, giving you confidence in your analytical skills, but also acquire expertise in implementing scalable machine learning solutions using industry-standard tools such as PySpark, ensuring that no data is too big or too complex for you. You will also have the opportunity to broaden your horizons through one of the first of its kind study of ethical topics posed by machine learning. You will graduate with an ability to go beyond the algorithms and turn data into actionable insights, contribute to strategic decision making in your organisation and become a responsible member of this rapidly growing profession.
Imperial, ranked #9 in the world by Times Higher Education, is home to numerous eminent world-famous researchers in machine learning, many of which will be contributing to this program. It has had a rich history in driving innovation since the beginning of this field: John Nelder, Professor at Imperial College, helped developed GenSim, the precursor to R and the first proper implementation of a general framework for regression. The university maintains close ties with industry and a number of pioneering tech companies, some of which will be contributing to the program by way of project ideas for your MSc thesis.
Here’s a sample of Specializations on Coursera from other Imperial College programs:
Mathematics for Machine Learning
Statistical Analysis with R for Public Health Specialization
Build and analyze neural networks with PyTorch
Understand and prevent biased sampling
Model monitoring in real-world applications
Create hierarchical models and use graphical modeling with PyMC3
Approach unstructured data analysis through Natural Language Processing, image classification, and object recognition
This degree offers multiple pathways to meet the needs of students with multiple backgrounds -- both students just starting a career in data science, and those already working in roles such as senior data analysts, bioinformatics scientists, statisticians or business analysts.
Graduates are likely to pursue roles as data scientists, machine learning engineers, natural language processing engineers, data engineers, bioinformatics or health data scientists, AI engineers, or software engineers. Possibilities extend beyond this list, however, as machine learning is slowly becoming indispensable in other fields, such as journalism or even tourism.
This is a rigorous programme: applicants are expected to have a quantitative undergraduate degree in a subject like computer science, math, statistics, economics, or physics.