The course will cover a number of methods for both prediction and classification problems using both supervised and unsupervised machine-learning techniques in R.
The course is available in four formats:
- Clients office
- Online (virtual classroom) with live tutoring
- Bespoke (group or 1:1 coaching)
100% of AIR income, beyond operational expense, is used towards education and research. Read more…
Explore machine learning
Machine learning is a branch of computer science that studies the design of algorithms that can learn. This course will allow you to get to grips with machine learning through the use of R in order to address problems and discover methods for the prediction and classification problems.
Designed for people who have either completed our Introduction to R course or who have experience using it, you’ll use supervised and unsupervised machine-learning techniques and apply them to a variety of real-world datasets, such as house price data or demographic data.
Throughout the course, you’ll learn the following techniques:
- Classification: Decision trees, logistic regression
- Numeric prediction: Regression trees, linear regression
- Pattern discovery: Association rules
- Clustering: K-means clustering
By the end of the course, you’ll have built a library of re-usable code and understand how to solve more common problems.
For this course, you’ll need some basic experience with R, be able to conduct simple statistical analysis. You also have the option to bring your own dataset if you have it.
On this course, we want you to have as much hands-on experience as possible. We’ll focus on teaching you to apply machine-learning techniques to data, rather than focusing on the more theoretical points. However, we will point you toward online content that will help you with the more theoretical side and prepare you for any future courses you decide to go on.
Campus Courses - VAT-free: Our venues are in central London (Senate House) and in New Cross. You can select your preferred location by choosing the date available.
Virtual Classroom Courses - VAT-free: Live tutoring brings all the benefits of face to face training and, in addition, improves skills for ‘digital’ participation. There is an increasing need for online activities in all areas of life – making ‘digital’ participation an essential skill of the 21st century.
Bespoke Courses: If you are interested in a tailor-made Public Speaking course option: 1-1 coaching or bespoke group training, please contact us using the email or phone number provided below.
Please see booking page for fees.
Please note our short courses sell out quickly, so early booking is advisable.
Find out more about all of the AIR courses available to you.
For all enquiries, and to be regularly updated on upcoming AIR courses, please email: air (@gold.ac.uk). For anything urgent, or if emailing is not a good option for you, you can also call us via WhatsApp or phone on +44 (0)7908 566 949.
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We are committed to providing reasonable teaching adjustments for students with disabilities that may impact on their learning experience. If you require adjustments, please complete the relevant section on the booking form and also contact us at firstname.lastname@example.org so we can respond to your requests as soon as possible.
Please note our short courses sell-out quickly, so early booking is advisable.
The course is supervised by Carlos Salas Najera, who has more than 15 years of experience in investments, data science and machine learning. Carlos has a BSc in Economics, MSc in Finance as well as postgraduate education in Data Science and Machine Learning at the New York City Data Science Academy. He is a holder of the CFA and CQF professional investment certificates. Carlos is an FCA-regulated C30 investment professional whose career spans investment firms such as Santander AM, BNP Paribas AM, LCAM and Jefferies International. In addition, Carlos is also deeply committed to education and research initiatives. He leads the CFA Machine Learning Working Group; and has advised the IFRS Foundation on new accounting standards.