Course dates

Tuesday 3 Oct 2023
6:30-8:30pm | 10 weeks

Course overview

This 10-week course is a practical introduction to machine learning using Python, one of the most widely used and in-demand programming languages. Taught live online, you’ll explore how to use statistical techniques and machine learning algorithms to create models that enable computer systems to learn from different types of data. These models can support decision-making in a range of fields, including market prediction, within scientific research and statistical analysis.

Open to anyone interested in machine learning and data science, this course is suitable for anyone who is interested in understanding the potential of machine learning when applied to datasets. The only requirement is a basic level of computer literacy, and this course is open to:

  • those with no prior knowledge or experience.
  • those with some coding experience.
  • experienced programmers.

The course is both theoretical and practical, and will ensure you understand the theory behind the algorithm, before you perform tests on real-world data examples. You'll be guided through the analysis of large amounts of data and classification of appropriate categories and taught how to recognise recurring features and identify correlations, so that you can develop a complex system which has the ability to make accurate predictions.

You'll begin by exploring the main tools provided in Python for data visualisation and data analysis, learning how to process data and create graphs that can reveal specific insights of a database. You’ll then delve into the theory and practical application of the main machine learning algorithm in Scikit-learn for classification and regression models. Following this you’ll begin to explore the sophisticated machine-learning library, Keras, which will expand on your work with Scikit-learn. The course will also cover some of the other most popular Python data science libraries including Jupyter Notebook, NumPy, pandas and matplotlib. Towards the end of the course, you'll have the opportunity to discuss Neural Networks and Convolutional Neural Networks to give you a brief introduction to the concept of Deep Learning.

Course structure

Taught through practical examples and theoretical explanation, the course will cover the following key aspects of Machine Learning: Data Pre-processing, Regression, Classification, Clustering, Introduction to Deep Learning. Specific topics include:

  • Installation of the Anaconda Distribution for running Python code, presentation of how machine learning is applied today in the industry, plus Python review.
  • Importing your dataset and make operation on your data using Pandas Python Library. Theory on features analysis and best practice in understanding the data.
  • Data Visualisation in Seaborn. Exploration of the most useful data visualisation to extract information from the database.
  • Linear regression in theory and practice.
  • Logistic Regression in theory and practice.
  • K Nearest Neighbours in theory and practice.
  • Decision Trees and Random Forests.
  • Support Vector Machine.
  • Unsupervised learning: K means and clustering.
  • Introduction to Neural Nets and Deep Learning.

Learning outcomes

  • Learn new skills that can further your career and improve your career prospects; Data Scientist is one of the most trending jobs in many economic sectors, and machine learning applied to data science is increasingly adopted due to its ability to solve problems relating to the increased availability of large datasets.
  • Gain an understanding of the theory behind the algorithm and experience testing your knowledge on real-world data examples.
  • Build your confidence, by learning how to ensure that there are no erroneous representations and rules of operation that lead to errors or misjudgements in your machine learning models.


Early bird price: £488.75 Standard price: £575. Please note that concessions cannot be applied to early-bird bookings.

Goldsmiths offers a 15% concession rate on short courses to Lewisham Local cardholders, Students and Goldsmiths Alumni.

Booking information

Please note our short courses sell out quickly, so early booking is advisable.

Tuesday 3 Oct 2023
6:30-8:30pm | 10 weeks




If you have any questions about this course please contact shortcourses (

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Disability Support

We are committed to providing reasonable teaching adjustments for students with disabilities that may impact on their learning experience. If you require adjustments, please contact us at so we can respond to your requests as soon as possible.

Tutor information

 Jamie A. Ward

Jamie A. Ward

Dr Jamie A Ward lectures on Machine Learning at Goldsmiths. His research lies at the intersection of Wearable Computing, Theatre, and Social Neuroscience. He is a visiting researcher at UCL, DFKI Kaiserslautern (Germany), and Keio University (Japan). He has worked at UCL’s Institute of Cognitive Neuroscience, and Lancaster University Computing Department (where he was a Marie Curie Fellow). He received his PhD from the electronics lab, ETH Zurich, in 2006, where he developed some of the first uses of multi-modal, wearable sensors for human activity recognition. He graduated with a degree in Engineering from the University of Edinburgh in 2000, and has worked on and off at various tech companies over the years. Jamie also trained and worked as a professional actor. For more details, see Jamie's website.


About the department

Our Department of Computing at Goldsmiths has a strong creative focus. We combine rigorous technical expertise with dynamic and innovative practice-based research. Our aim is to interrogate the theoretical landscape, while simultaneously applying this learning and challenging ourselves and our students to explore the technological boundaries. This stimulating environment is both creative and socially aware. On a number of our degree programmes there is an interdisciplinary approach, an aspect which is evidenced in our postgraduate and postdoctoral research community. This is a unique learning environment at the forefront of enabling students to become the programmers of tomorrow. 

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