Combining the fields of statistics, optimisation, data mining and computing, machine learning is being adopted across industries due to its ability to solve problems in the presence of big data sets. Recent successes of machine learning include its application in commercial tasks such as search engines, recommendation systems (for example Netflix and Amazon), and marketing. Machine learning methods are also increasingly being used in financial institutions for algorithmic trading, predicting customer behaviour, compliance and risk.
This eight-week online technical course from the London School of Economics and Political Science (LSE) covers a wide range of machine learning methods, following a practical approach to machine learning in modern business analytics. Throughout the course, you’ll get the opportunity to engage with real-world problems as you apply machine learning models to data sets in R, interpret the predictions, and evaluate these predictions to inform business decisions.
This course is technical in nature. It makes use of coding in R and covers the application of machine learning in business. Some algebraic and calculus knowledge is strongly advised, but is not required. Tertiary level statistics and knowledge of a functional or object oriented language is advantageous. HTML is not considered a programming language in this context. No specific software is required for this online certificate course.
What will set you apart
Earn a certificate of competence from LSE on this online certificate course and be empowered to:
- Make more informed business decisions and solve complex problems by learning how different machine learning models can be applied to a variety of data sets
- Implement various machine learning techniques, including regression, variable selection, shrinkage methods, classification, dimension reduction, and unsupervised learning
- Upgrade your mathematics and statistics knowledge, and learn the foundations of coding in R
- Explore the latest frontiers of machine learning, such as neural networks, and how these can be applied to your business context
This Machine Learning: Practical Applications online certificate course is certified by the United Kingdom CPD Certification Service, and may be applicable to individuals who are members of, or are associated with, UK-based professional bodies.
Note: should you wish to claim CPD activity, the onus is upon you. The London School of Economics and Political Science (LSE) and GetSmarter accept no responsibility, and cannot be held responsible, for the claiming or validation of hours or points.
Implement machine learning techniques to solve business problems and inform decision-making as you follow your learning path through the weekly modules of this online course:
- Orientation module Welcome to your Online Campus
- Module 1 Learning from data
- Module 2 Principles of machine learning
- Module 3 Regression
- Module 4 Variable selection and shrinkage methods
- Module 5 Classification
- Module 6 Tree-based methods and ensemble learning
- Module 7 Introduction to neural networks
- Module 8 Unsupervised learning
Develop technical machine learning competencies
This LSE online certificate course is delivered in collaboration with GetSmarter. Learn from industry thought leaders as you address business problems by analysing past trends and predicting future events using machine learning models for analytics.
Your Course Convenors
The design of this online course is guided by LSE faculty and industry experts who will share their experience and in-depth subject knowledge with you throughout the course.
DR KOSTAS KALOGEROPOULOS
Associate Professor of Statistics, London School of Economics and Political Science
With a focus on developing and applying advanced computational and machine learning methods, Kostas’s research methodology has mostly targeted continuous time probability models based on stochastic differential equations driven by standard or fractional Brownian motion. Other areas include factor analysis and sequential learning. His research finds applications in financial and econometric time series, as well as biomedical problems such as stochastic epidemic models and analysis of growth curves.
Prior to joining the statistics department at LSE, he was a postdoctoral researcher at the University of Cambridge in the Signal Processing Laboratory of the engineering department. He completed his PhD (2007) in the statistics department of the Athens University of Economics and Business while spending some time at the University of Lancaster.
DR YINING CHEN
Assistant Professor of Statistics, London School of Economics and Political Science
Yining's current research focuses on developing new methods for statistical problems, such as change-point detection and nonparametric estimation. He is also interested in understanding the computational aspects of statistical methods. He completed his PhD (2014) in statistics at the University of Cambridge.
DR XINGHAO QIAO
Assistant Professor of Statistics, London School of Economics and Political Science
Xinghao’s research is extensive. He is focused on functional and longitudinal data analysis as well as high dimensional statistical inference such as covariance and precision matrix estimation, and variable selection. He’s further interested in time series analysis such as functional time series and high dimensional time series. Xinghao also analyses statistical machine learning with applications in business, neuroimaging analysis and environmental sciences.
Prior to joining LSE as an assistant professor in statistics, Xinghao earned his PhD in business statistics from Marshall School of Business at the University of Southern California, his MS in statistics at the University of Chicago, and BS in mathematics and physics at Tsinghua University.
Earn a certificate from LSE
Get recognised for your knowledge when you earn an LSE certificate of competence – and use it to set yourself apart as you evaluate machine learning predictions to inform your business decisions.
Your certificate will be issued in your legal name and sent to you upon successful completion of the course, as per the stipulated requirements.REGISTER NOW
Get More Information
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WHAT ARE MY PROGRAMME PAYMENT OPTIONS
When paying for your programme, you can choose to pay the full amount, prior to programme start, or you can opt for a two-part payment plan option when you register.
The two-part payment option requires one payment up front, followed by a second payment during your programme.
If you want to find out more about payment options or the programme itself, please get in contact with an Enrolment Advisor here.
WILL I HAVE THE TIME?
GetSmarter's learning model is designed to help you, as a working professional, improve your skills without compromising on work and family responsibilities. The course work is broken up into weekly, manageable bite-sized modules, with incremental deadlines, designed to help you pace yourself over the duration of the course and allow you the legroom to work when it suits you best.
At the beginning of each week you'll be presented with all the lectures, notes and assignments necessary for completion. You also have access to your Success Advisor who will help you set goals and track key milestones, manage your time, and field any administrative requests you might have.
CAN MY EMPLOYER ASSIST WITH PAYMENT?
By improving your skills and industry knowledge, you'll be having an influence on the success of your organisation.
Why wouldn't you ask your boss to help you fund your studies if it's going to have an impact on the way you do business?
37% of our past students receive financial assistance from their employers. You can ask for help, too. Here is a guide to show you how to receive financial assistance from your employer.
WHAT IS THE ONLINE CAMPUS?
The Online Campus will be your virtual classroom for the duration of your course. Through its easy-to-use interface you'll have access to a diverse variety of course content formats, including: interactive video lectures, module notes, practice quizzes, Prezis, assignment briefs, and additional web resources.
On the Online Campus, you'll also be able to ask questions and interact with your fellow students and Head Tutor through the Online Campus discussion forums. If you're experiencing tech issues or need advice from your personal Success Advisor, you can use the Online Campus to get in touch.