Course description
Machine Learning: Practical Applications
Earn an official certificate of professional achievement from The London School of Economics and Political Science
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 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.
Suitability - Who should attend?
Is this course for you?
This online certificate course is designed for professionals looking to improve the data analytics of an organisation through the integration of machine learning techniques. Mid to senior managers, data specialists, consultants, analysts, IT, and business professionals who are interested in the practical applications of machine learning will benefit from the in-depth exploration of core principles and methods. If you are interested in upskilling, transitioning into a data science role, or wanting to improve your understanding of the business applications of data science in any industry or business area, this course will assist you in developing and validating your practical machine learning skills and knowledge.
Prerequisites
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. Training in tertiary-level statistics and knowledge of a functional or object-oriented language are advantageous. HTML is not considered a programming language in this context. No specific software is required for this online certificate course.
Outcome / Qualification etc.
- Develop technical machine learning skills and earn an official certificate of competence from the London School of Economics and Political Science.
- Assessment is continuous and based on a series of practical assignments completed online. In order to be issued with a certificate, you’ll need to meet the requirements outlined in the course handbook. The handbook will be made available to you as soon as you begin the course.
- Your certificate will be issued in your legal name and sent to you upon successful completion of the course, as per the stipulated requirements.
What will set you apart
On completion of this course, you’ll walk away with:
- The confidence to make more informed business decisions and solve complex problems by understanding how different machine learning models can be applied to a variety of data sets.
- The skills to implement various machine learning techniques, including regression, variable selection, shrinkage methods, classification, dimension reduction, and unsupervised learning.
- Upgraded mathematics and statistics knowledge, and the foundations of coding in R.
- Knowledge of the latest frontiers of machine learning, such as neural networks, and how these can be applied to your business context.
- Unlimited access to 2U’s Career Engagement Network, offering you exclusive resources and events to support your professional journey and drive your career forward.
Training Course Content
Implement machine learning techniques to solve business problems and inform decision-making as you work through the weekly modules of this online certificate course.
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
Course delivery details
Duration
- 8 weeks (excluding orientation)
- 8-10 hours per week Self-paced learning online