Programmes

Machine Learning: Practical Applications

  • Online certificate course
  • Department of Statistics
  • Application code OCC-MNL
  • Starting 2020
  • Short course: Open
  • Location: Global

Machine learning Course-Page-image

Machine learning is becoming an increasingly important analytical tool, enabling  businesses to extract meaningful information from raw data, offering accurate analyses and complex solutions to data-rich problems. The Machine Learning: Practical Applications online certificate course from the London School of Economics and Political Science (LSE) focuses on the practical applications of machine learning in modern business analytics and equips you with the technical skills and knowledge to apply machine learning techniques to real-world business problems. 

Divided into two parts, the first part of the course explores how to learn from data, introducing you to the core principles of machine learning. During the second part of the course, you’ll gain an in-depth understanding of a variety of machine learning techniques that you can apply when analysing big data including regression, variable selection and shrinkage methods, classification, tree-based methods, ensemble learning, unsupervised learning, and an introduction to neural networks. Over the course of eight weeks, you’ll learn how to match a suitable machine learning technique to a particular problem to make accurate predictions and inform business decisions.

Understand how these methods can help data scientists, business leaders, analysts, and professionals problem-solve and innovate through informed, data-driven decision-making.


CPD Certified

This 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. The course has an estimated 75 hours of learning. 

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.

Programme details

Key facts

Registrations close: 25 August 2020

Course start (Orientation opens): 02 September 2020

Module 1 starts: 09 September 2020

Duration: 8 weeks (excluding orientation)

Commitment: 7–10 hours per week

Fee: £2,100

Course outcomes

  • Gain insight into the business applications of machine learning 

  • Develop the technical and practical skills to apply machine learning to solve real-world problems in your business context

  • Understand the fundamental principles of machine learning and the flow of the machine learning pipeline

  • Learn to code in R and apply machine learning techniques to various types of data

  • Maximise team productivity and unlock new efficiencies by implementing machine learning in business

  • Explore regression as a supervised machine learning technique to predict a continuous variable (response or target) from a set of other variables (features or predictors)

  • Discover how variable selection and shrinkage methods are used to improve the efficiency of a regression model when applied to complex data sets

  • Explore classification as a supervised machine learning technique to predict binary (or discrete) response variables from a set of features

  • Discover how tree-based methods and ensemble learning methods are applied to improve the accuracy of a prediction

  • Understand what neural networks are, its most successful applications, and how it can be used within a business context

Programme structure

  • Orientation Module
  • 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

Faculty

Convenors:

Dr Kostas Kalogeropoulos - Associate Professor of Statistics, London School of Economics and Political Science 

Dr Yining Chen - Assistant Professor of Statistics, London School of Economics and Political Science

Dr Xinghao Qiao - Assistant Professor of Statistics, London School of Economics and Political Science

 

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. 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.

Teaching

This course is presented entirely online, in collaboration with leaders in online education, GetSmarter, a brand of 2U, Inc. View this Machine Learning: Practical Applications online certificate course on the GetSmarter website.

Modules are released on a weekly basis, and can be completed in your own time and at your own pace. 

This interactive, supportive teaching model is designed for busy professionals and results in unprecedented certification rates for online courses.

Reading materials

All reading materials for this course will be made accessible to you through the Online Campus. Notes for each module are often downloadable, and you are able to save any information or collateral you use to your personal profile.

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