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AI and Machine Learning for Business

This course will introduce you to the core capabilities of Artificial Intelligence (AI) and empower you to contribute to this exciting and transformational new era in global technological development.

Key benefits:

  • Tutor-led - University of Southampton academics will guide you through the content and answer your questions, unlike other online courses where you're left to your own devices
  • Continuing Professional Development (CPD) accredited, helping you demonstrate your commitment to upskilling at your next work appraisal
  • Hands-on - learn how to apply AI capabilities within your own workplace.

Developed by the pioneering Data Science team at the University of Southampton – ranked among the top 100 universities globally – this course will equip you with the specialist knowledge and skills you need to understand and effectively apply AI technologies within your organisation.

Taught by industry-leading experts in AI and data science using a practical approach to learning, this 6-week flexible online course will see you exploring a range of AI capabilities in the context of real-life business case studies – teaching you how you can effectively harness the power of AI to transform your business.

What you’ll learn

The course runs over 6 weeks and is broken down into manageable weekly topics:

Week 1: Introduction to AI

  • What AI is and its main classes of applications and capabilities
  • The differences between different types of AI technologies
  • Core technologies associated with AI
  • The relationship between AI and other technology trends such as big data, cloud computing, and the Internet of Things (IoT)
  • The role of data in AI
  • The challenges in applying AI within organisations
  • The limitations of AI

Week 2: Case study – Learning to know your customers

  • The difference between supervised and unsupervised machine-learning algorithms
  • Fundamental classes of machine-learning, including regression, classification and clustering
  • Types of business problems machine-learning can solve and machine-learning tasks that can be used to solve them
  • Activities and technologies used to build a Natural Language Processing (NLP) pipeline
  • Statistical processing and work distributions
  • Applying regression, classification, and clustering to extract information and recommend items to purchase
  • Analysis, assessment and interpretation of the results of machine-learning models

Week 3: Case study – Enhancing the customer experience

  • The Turing test and how it can be used to improve AI systems
  • Important methods and technologies in natural language generation
  • Deep-learning approaches to NLP and what they’re used for
  • Important methods and tools in natural language understanding and speech recognition
  • Designing conversational agents (i.e. chatbots)

Week 4: Case study – Search and recommendation

  • Clustering algorithms
  • Topic modelling
  • Knowledge bases: How are they built? What purpose do they serve?
  • Using a knowledge base for Named Entity Recognition (NER)
  • Introduction to the semantic web
  • Using the knowledge base to extract relevant information (i.e. SPARQL and Google Knowledge Graph)

Week 5: Case study – Computer vision

  • Traditional approaches to image-processing and computer vision
  • Image classification and clustering
  • Feature extraction
  • Convolutional neural networks (CNNs)
  • Combining CNNs with conversational agents to generate textual descriptions
  • Systems for automatic surveillance

Week 6: Future directions for AI

  • Current limitations
  • Technological advances
  • Societal and cultural shifts
  • Ethical, moral and legal issues

After successfully completing the course, you’ll be able to:

  • Understand what AI technology is, its capabilities and limitations, and the potential benefits it can bring to your business
  • Identify AI’s main capabilities and the relevant technologies needed to deliver them
  • Explain the different components needed to deliver complex AI systems
  • Discuss the ethical, moral and legal implications of AI in various areas of today’s society
  • Identify different types and applications of data in delivering effective AI solutions
  • Identify various software that can be used to process, analyse, and draw meaning from natural language as well as from images and numerical data – enabling deeper insights

How you'll learn

Guidance thoughout the course

A key benefit of choosing Southampton Data Science Academy over some of the other online courses available is that our courses are tutor-led. A tutor is just as important with an online course as it is in a physical classroom. A good tutor’s passion for the subject will motivate you and inspire you, making the content stick in your mind.

Our tutors are data science experts who can make complex ideas accessible. If you don’t understand the course material right away they can provide an alternative explanation or use a different example. If you have a question about the content, our tutors are available to answer it. They will work with you to make sure you understand the subject fully and are on track to complete the course successfully.

Hands-on learning that you can immediately apply to your work

Learning is hands-on, using real-life business examples to demonstrate how you can immediately harness and apply the power of data science to your work. Our online learning platform is easy to access via smartphone, tablet or desktop – anytime and from anywhere in the world. You’ll join a global online network of like-minded professionals and take part in group discussions, Q&A sessions and video tutorials.

Our courses are also CPD-accredited. Many employers will ask you at appraisals to show evidence of the impact of Continual Professional Development (CPD) on your professional work. A CPD-accredited Southampton Data Science course is a great way to demonstrate your commitment to upskilling, especially as you will learn practical techniques and tools that you can immediately apply to your workplace.

Find out more about how you'll learn with Southampton Data Science Academy.

Professor

Les Carr

Les is Professor of Web Science at the University of Southampton’s School of Electronics and Computer Science. He’s also a Director of the Web Science Institute and former Director of the Web Science Centre for Doctoral Training. His research on Open Access and Open Data led to the establishment of Eprints – offering Open Access publication and data services, training and support to the research industry.

Professor

Elena Simperl

Elena is a Professor of Computer Science within the Web and Internet Science research group – part of the University of Southampton’s School of Electronics and Computer Science. Her primary research domain concerns the intersection between knowledge technologies and crowd computing. Elena is interested in socially and economically-motivated aspects of creating and using semantically-enabled content on the Web, and in paradigms, methods and techniques to facilitate large-scale collaboration and incentivise participation.

Dr

Rob Blair

Rob is Visiting Fellow at the University of Southampton’s School of Electronics and Computer Science. He holds an MSc Information Systems from the University of East Anglia and an MSc Web Science from the University of Southampton. Qualified to teach Physics, Mathematics and Computer Science, Rob is a highly-experienced classroom teacher and online tutor specialising in data science.

Dr

David Millard

Dave is Associate Professor of Computer and Web Science at the University of Southampton, David is a founding member of the Web and Internet Science research group within the School of Electronics and Computer Science (ECS). He represents ECS on the steering group for the Web Science Centre for Doctoral Training. David is currently Vice-Chair for ACM SIGWEB.

Dr

Manuel León Urrutia

Dr. Manuel León Urrutia is Research Fellow in the Web and Internet Science research group of the Electronics and Computer Science department of the University of Southampton. Manuel is specialised in learning technologies, with experience in learning design and research interests in MOOCs and learning analytics. Prior to joining the Computer Science department in 2012, he worked for 6 years as an editor in a publishing company, and 6 more years as a language teacher in the University of Southampton. Manuel currently has an academic, senior tutoring and learning design role in the Southampton Data Science Academy.

Dr

Gary Wills

Gary is an Associate Professor in Computer Science at the University of Southampton. He graduated from the University of Southampton with an Honours degree in Electromechanical Engineering, followed by a PhD in Industrial Hypermedia Systems. Gary is a Chartered Engineer, a member of the Institute of Engineering Technology, and a Principal Fellow of the Higher Educational Academy. He is also a visiting Associate Professor at University of Cape Town and a research professor at RLabs.

Ready to boost your data literacy?

Take our flexible online course and learn how to leverage data to achieve your business goals.

Apply now