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Practical Machine Learning

Short name: PML
SITS code: BUCI078H6
Credits: 15
Level: 6
Module leader: Paul Yoo
Lecturer(s): Paul Yoo
Online material:

Module outline

This module will not be offered until the 2021/22 academic year.

This module covers the fundamental concepts and techniques of applied machine learning using Python and how to use the existing tools to analyse data. Students develop the hands-on and practical skills needed for applied machine learning including the use of existing Python libraries and tools (e.g. Scikit-Learn and Keras) and the use of the techniques needed to analyse data (e.g. pre-processing, feature selection and classification). The module will use Python the most popular machine learning language to solve practical problems based on use cases extracted from real domains such as financial forecasting and computer vision.


  • To gain hands-on and practical skills for machine learning based analytics tasks
  • To use appropriate Python libraries and tools to analyse data
  • To develop the design and programming skills that will help you to build intelligent artefacts
  • To assess the performance of machine learning models
  • To develop a deeper understanding of several real-life topics in applied machine learning
  • To develop the practical skills necessary to pursue research in applied machine learning

Learning Outcomes

On successful completion of this module a student will be expected to be able to:

  • Identify and use Python tools and libraries for machine learning based analytics tasks
  • Evaluate and identify appropriate machine learning methods and techniques to analyse data
  • Critically Analyse and interpret machine learning results
  • Use machine learning tools to solve practical problems in real-life scenarios
  • Demonstrate deep understanding of a range of complex real-life topics in applied machine learning


  • Introduction to Python for machine learning
  • Preparing data
  • Feature selection for machine learning
  • Evaluation and resampling
  • Rule-based algorithms: decision tree and random forest
  • Regression-based algorithms: logistic regression and neural networks
  • Large-scale machine learning using TensorFlow
  • Real-life case studies (e.g. financial forecasting and computer vision)


None but basic Python programming skills are required.


Indicative timetables can be found in the handbooks available on programme pages. Personalised teaching timetables for students are available via My Birkbeck.


Coursework (30%) and written examination (70%)

Recommended reading

Géron, Aurélien. Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems. O'Reilly Media, 2019.