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Artificial Intelligence and Machine Learning for Business

University of Southampton - Offered by CEG Digital, United Kingdom
Overview Course Requirements Syllabus
Artificial Intelligence and Machine Learning for Business
Next enrollment cycle March 2025 See all cycles
Total Cost KES 314,650
Course Accredited By PC
6 Weeks Online Postgraduate Certificate

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

Key benefits:

  • Tutor-led - University of Southampton academics will guide you through the content and answer your questions, providing the support you need
  • Hands-on - learn how to apply AI capabilities within your own workplace
  • Developed by our pioneering Data Science team - the University of Southampton is ranked among the top 100 universities globally.

Who is this course for?

  • This 6-week, part-time online course is for professionals who want the knowledge and skills required to understand and apply AI technologies in the workplace. You will learn how your organisation can use AI to deliver benefits, improve internal processes and business models, and create new commercial opportunities. It will help you understand a quickly evolving field, cutting through marketing jargon to find practical solutions to real business needs.
  • The course is organised according to a framework of core AI capabilities. It follows a problem-based learning approach, where each AI capability is discussed in the context of a business case study. One way or another, AI will determine the future of technology. This course teaches you what you need to know to be part of this journey and to effectively harness the power of AI to transform your business.

Guidance throughout 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.

Assessment methods and feedback

  • You'll complete four assignments. Your online tutor will provide feedback at the end of the course, consisting of marks and comments on each assignment. After passing the course, you'll receive a downloadable electronic certificate.

Enrollment Cycles

  • March 2025

Entry Requirements

  • Basic understanding of technology. Previous coding experience is not essential

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

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