Machine Learning: The science of teaching computers to act

Machine learning is the science of training computers to act without explicit programming. In this course, we provide a technical introduction to Machine Learning (ML), which is becoming increasingly important in business, from managing daily operations to developing and executing commercial strategies. The majority of recent developments in Artificial Intelligence (AI) can be directly attributed to improvements in ML. Some also believe that ML will be the key to unlocking human-level AI. On a more practical note, companies are already transforming existing business practices using ML, through the creation of recommendation engines, chatbots triaging customer queries, automated insight generation and end-to-end automation of data mining and modelling.


Who will find it useful

  • This course will benefit more technically-minded analysts and market research agency practitioners, who want to better understand the increasingly important field of machine learning and its role in business. Beginners are welcome as this is an introductory course to machine learning.



  • Understand the relationship between Machine Learning and Artificial Intelligence
  • To inform delegates of the main opportunities and risks of deploying ML models
  • Outline the differences between supervised and unsupervised learning
  • Highlight the importance of rigorous cross-validation protocols
  • Provide a quick introduction to Python programming so that delegates can implement key ML algorithms following the training session
  • Develop an intuition of the types of business problems ML can tackle


Learning outcomes

  • Technical and conceptual awareness of neural networks, machine learning and deep learning
  • Develop a theoretical and practical understanding of popular ML techniques, including Neural Networks, Deep Learning and XGBoost
  • Gain an awareness of the history of ML and key contemporary developments
  • Deciding which ML models are most suitable for particular analytical challenges



The Old Trading House, 15 Northburgh Street,London,EC1V 0JR

Aji is VP of Data and Research at Chattermill, a start-up based in London specialising in applying artificial neural networks to customer feedback data to help make more customer-centric decisions. At Chattermill, Aji is responsible for a team of data scientists, machine learning engineers and research scientists. Before joining Chatermill, Aji spent five years at Sky, as Head of Data Science and has a total of 10 years of experience in Research and Data Science. He is also extremely passionate about all things related to Neuroscience, Artificial Intelligence and Robotics. Aji holds a PhD in Computational Cognitive Science, specialising in developing large-scale machine learning simulations of semantic processing (text and image). He is also a Research Fellow at the Centre for Cognition, Computation and Modelling at Birkbeck, University of London. Aji regularly lectures for the Market Research Society on a wide range of topics such as Machine Learning, Artificial Intelligence, Data Science and Neuroscience.

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