Machine Learning and Artificial Intelligence in Python



Overview

Data science is a discipline that uses scientific methods, processes and algorithms to extract meaningful information, knowledge and insights from structured and unstructured data.

The aim of this course is to provide insights on intermediate and advanced data science topics, using the Python programming language. The course will explore concepts including machine learning, deep learning and natural language processing from a practical hands-down point of view. The focus will be on tools and methods rather than diving into the theoretical basis, in order to be appreciated by an audience with a minimal mathematical background.

Experience in using a programming or scripting language is a must. The student should have mastered all the concepts explored in the course Python Programming for Data Science: Introduction.

In order to complete the assignment (and in order to get the full benefit from the course) students will need access to a computer capable of running the open-source software used in the course and access to the Internet. A limited amount of class time will be allocated to working on the class assignment, so students should ensure that they have access to a computer outside of class.

The course will rely on Jupyter Notebooks for interactive Python programming as they are widely used in Data Science.

 

Before attending this course, prospective students will know:

  • All the requirements and topics covered in the "Python Programming for Data Science: Introduction" course, i.e:
    • The fundamentals of linear algebra: what is a matrix and how matrix addition and multiplication are performed.
    • The following fundamental concepts of statistics: mean, median, variance and standard deviation, interquartile range.
    • The fundamentals of algebra: exponential and logarithm, and trigonometric functions.
    • How to perform fundamental Python operations such as variable creation, numerical operations on scalar, vectors and matrices, iteration through a collection, manipulation of elements in a collection.
    • How to use NumPy and pandas to import a dataset and extract important statistics from it using techniques such as split-apply-combine (for example, finding the mean, median or max of a quantitative variable for each category in a categorical variable).
    • Given a dataset, how to select the appropriate visualisation graph depending on the information to be conveyed, and use the matplotlib and seaborn library to draw it and add title, captions and figure legends.
    • How to create and add state and behaviour to a class in Python.
    • What is, at least conceptually or visually, a derivative and a gradient.

Programme details

Courses starts: 21 Jan 2026

Week 1: Introduction to the course. Basic overview of Machine Learning. Linear Regression example.

Week 2: Overview of a data-science preprocessing pipeline. Exploratory Data Analysis (EDA)

Week 3: Data cleaning and preparation. Feature engineering

Week 4: Supervised learning: regression.

Week 5: Supervised learning: binary classification. 

Week 6: Supervised learning: multi-class and multi-label classification

Week 7: Decision Trees. Ensemble Methods. Hyperparameter Tuning.

Week 8: Unsupervised Learning for dimensionality reduction and clustering.

Week 9: Introduction to neural networks and deep learning.

Week 10: Convolutional neural networks (CNNs). Introduction to other deep networks: Recurrent Neural Networks (RNNs), Transformers.

The following Python libraries will be used during the course:

  • scikit-learn (weeks 2-8)
  • PyTorch (weeks 9-10)
  • NumPy pandas, matplotlib, seaborn (throughout the course)

Certification

Credit Accumulation Transfer Scheme (CATS) Points

Only those who have registered for assessment and accreditation will be awarded CATS points for completing work to the required standard. Please note that assignments are not graded but are marked either pass or fail. Please follow this link for more information on Credit Accumulation Transfer Scheme (CATS) points

Digital Certificate of Completion 

Students who are registered for assessment and accreditation and pass their final assignment will also be eligible for a digital Certificate of Completion. Information on how to access the digital certificate will be emailed to you after the end of the course. The certificate will show your name, the course title and the dates of the course attended. You will be able to download the certificate and share it on social media if you choose to do so.

Please note students who do not register for assessment and accreditation during the enrolment process will not be able to do so after the course has begun.

Fees

Description Costs
Course fee (with no assessment) £340.00
Assessment and Accreditation fee £60.00

Funding

If you are in receipt of a UK state benefit, you are a full-time student in the UK or a student on a low income, you may be eligible for a reduction of 50% of tuition fees. See details of our concessionary fees for short courses.

Tutor

Dr Massi Izzo

Massi is a Senior Software Engineer at Mind Foundry and a part-time Departmental Tutor at Oxford University Department for Continuing Education. His fields of expertise are Python Programming, Machine Learning Engineering and Full Stack Development.

Course aims

1. Explore the landscape of contemporary machine learning (ML) and artificial intelligence.

2. Learn how to use a variety of machine learning algorithms to extract features from the data using Python libraries.

3. Familiarise with the concepts of overfitting and regularisation in ML

4. Gain insights on how to face scaling issues in a "big data" scenario.

Teaching methods

Each week's session will consist of lectures and hands-on programming exercises, class discussions and interactive programming demonstrations by the lecturer.  

Learning outcomes

A the end of the course the students will be able to:

  • choose the right ML task and evaluation metric for a given ML problem and select a set of ML models to be trained;
  • set up a data pre-processing pipeline for data science and machine learning algorithms;
  • use Python machine learning tools (namely scikit-learn and PyTorch) to build up ML models, train and evaluate them on a test set;
  • evaluate whether a model overfits or underfits the data and act accordingly (e.g. opportunely regularise and overfitting model);
  • identify the appropriate and most performant model for a given task and tune appropriately the hyperparameters (parameters that cannot be learned by the model).

Assessment methods

Students will be asked to submit an assignment where they develop a machine learning pipeline comprising all the steps discussed in the course: EDA, data preparation, model training, model evaluation. To facilitate the assignment submission, students will be offered an early submission by week 6 for the first two activities (EDA and data preparation) and by week 9 for model training and evaluation. 

In order to complete the assignment (and in order to get the full benefit from the course) students will need access to a computer capable of running the open source software used in the course and access to the Internet. Only a limited amount of class time will be allocated to working on the assignment, so students should ensure that they have access to a computer outside of class.

Only those students who have registered for assessment and accreditation will submit coursework.

Application

How to enrol

Please use the 'Book now' button on this page. Alternatively, please complete an enrolment form.

How to register for accreditation and assessment

To be able to submit coursework and to earn credit (CATS points) for this course, if you wish to do so, you will need to register and pay an additional £60 fee. You can do this by ticking the relevant box at the bottom of the enrolment form or when enrolling online. 

Students who do not register for CATS points during the enrolment process will not be able to do so after the course has begun.

If you are enrolled on the Certificate of Higher Education at the Department you need to indicate this on the enrolment form but there is no additional registration fee.

Level and demands

The Department's Weekly Classes are taught at FHEQ Level 4, i.e. first year undergraduate level, and you will be expected to engage in a significant amount of private study in preparation for the classes. This may take the form, for instance, of reading and analysing set texts, responding to questions or tasks, or preparing work to present in class.

IT requirements

In order to complete the assignment (and in order to get the full benefit from the course), students will need access to a computer capable of running the open-source software used in the course and access to the Internet. Only a limited amount of class time will be allocated to working on the assignment, so students should ensure that they have access to a computer outside of class.

Terms & conditions for applicants and students

Information on financial support