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 programme, 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 programme. 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 programme by way of project ideas for your MSc thesis.Here’s a sample of Specializations on Coursera from other Imperial College programmes:. Who is this degree for: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. Imperial College London is the UK’s only university to focus solely on science, engineering, medicine, and business.
In 2011, Stanford professor Andrew Ng launched his first MOOC, teaching just over 100,000 students about machine learning. Ng has said that the course, which turned out to be very popular, led to the start of Coursera.Today, Coursera reaches 7.5 millions of users around the world, and Ng’s machine learning course continues to receive wildly positive reviews from seasoned and newly initiated. Machine Learning (Coursera) This is my solution to all the programming assignments and quizzes of Machine-Learning (Coursera) taught by Andrew Ng. After completing this course you will get a broad idea of Machine learning algorithms. Try to solve all the assignments by yourself first, but if you get stuck somewhere then feel free to browse the code.
Consistently ranked amongst the top 10 universities in the world, Imperial is home to a global community of scientists, engineers, medics, and business experts.This research-led approach shapes the way they educate students through teaching that opens everything up to question. It’s a style of learning that relies on learning by discovery and prepares graduates to bring fresh perspectives to the ever-evolving landscape of technology. Coursera does not grant credit, and does not represent that any institution other than the degree granting institution will recognize the credit or credential awarded by the institution; the decision to grant, accept, or transfer credit is subject to the sole and absolute discretion of an educational institution.If upon graduation you intend to pursue a PhD or apply for employment which requires a master-level degree beyond 90 ECTS credits, we encourage you to investigate whether this program meets your academic and/or professional needs before applying.