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Computer Science

We are pleased to offer a microcredential in the Applications of Machine Learning, for those interested in gaining practical experience in machine learning.

Duration: 12 weeks
Cost: £795
Level: Postgraduate

Machine learning lies at the basis of many cutting-edge technologies in AI, from self-driving cars to smarter search engines. Now, businesses across industries are discovering its benefits.

This microcredential will help you understand how machine learning can help businesses in many ways – from reducing costs and improving customer experiences,to accelerating innovation.

Through tutorials, exercises, and labs, you'll gain hands-on experience with machine learning applications. With this knowledge, you’ll be able to maximise the potential of machine learning in your organisation.

We'll connect you with industry leaders and explore real-world case studies. This practical approach will solidify your understanding of machine learning fundamentals and show you how to apply them in real-life scenarios.

Entry requirements

This microcredential is designed for a postgraduate level and it is recommended that you have completed a first degree in Computer Science, or similar, or have equivalent coding experience.

You should also have skills in Python or other common data science packages, linear algebra, and probability and statistics.

You’ll need a laptop or desktop computer with an internet connection to access the lab elements of this microcredential through Google’s Colab. Access to a Python platform such as Anaconda is helpful but not required.

This microcredential is designed for those interested in upskilling in the area of machine learning and is suitable for a variety of sectors.

You might be a data scientist or data analyst looking to develop your role to include machine learning or you could looking to specialise further in your field and apply machine learning to solve specific real-world issues.

Start date

We aim to run our microcredentials every few months.

Join on the date that suits you or register with FutureLearn to hear about future runs and updates.

Register now

Course details

You’ll finish the microcredential with both theoretical and practical knowledge of machine learning methods and have the confidence to immediately start using this technology to excel in your career.

On this course, you will:

  • Learn to identify and understand a variety of machine learning methods
  • Master the fundamentals of data preprocessing
  • Understand how to implement machine learning techniques
  • Explore linear models of machine learning methods
  • Improve your data analyst skills alongside industry experts

Find out more about the course details.

Learning outcomes

You’ll finish with a machine learning qualification from Cardiff University. This is 15 UK Credits at Postgraduate level.

You will develop skills in:

  • Data pre-processing in Python
  • Data representation
  • Use machine learning libraries
  • Choose machine learning tools
  • Implement machine learning tools
  • Critical evaluation of machine learning methods

Modules

This course introduces you to the concept of machine learning and runs through basic data preprocessing in Python.

Duration: 4 weeks

Week 1: Machine learning fundamentals

  • Welcome to applications of machine learning
  • Wrapping up the week
  • Machine learning platforms
  • Machine learning approaches
  • Machine learning pipelines
  • Introduction to machine learning

Week 2: Basic data preprocessing

  • Welcome to Week 2
  • Introduction to data preprocessing
  • Feature engineering, selection, and extraction
  • Feature scaling
  • Wrapping up the week

Week 3: Basic data preprocessing

  • Welcome to Week 3
  • Categorical feature encoding
  • Dealing with missing data
  • Wrapping up the week

Week 4: Basic data preprocessing

  • Welcome to Week 4
  • Dimensionality reduction
  • Learning from Imbalanced Data
  • Wrapping up this course

Learn how to design and evaluate machine learning experiments and discover the importance of eliminating bias in experiments.

Duration: 2 weeks for 3 hours per week

Week 1: Designing machine learning methods

  • Welcome to evaluating machine learning
  • Designing machine learning experiments
  • Performance assessment methods
  • Evaluation metrics
  • Wrapping up the week


Week 2: Bias and ethics in machine learning

  • Welcome to week 2
  • Bias in machine learning
  • Ethics and bias
  • Wrapping up the week

Discover the traditional machine learning approach: linear machine learning models and theory.

Duration: 2 weeks

Week 1:  Introduction to linear models

  • Welcome to linear models and support vector machines
  • Linear Regression
  • Iterative optimisation methods
  • Increasing model complexity
  • Wrapping up the week

Week 2: Regularised linear models and support vector machines

  • Welcome to Week 2
  • Regularised linear models
  • Logistic regression
  • Support vector machines
  • Wrapping up this course

This course will help you unpack neural networks and explore examples of standard neural network architectures.

Duration: 4 weeks

Week 1: Ensemble learning

  • Welcome to Ensemble Learning and neural networks
  • Decision trees
  • Ensemble learning
  • Random Forest and AdaBoost
  • Wrapping up the week

Week 2: Neural networks 1

  • Welcome to week 2
  • Introduction to neural networks
  • Optimisation in neural networks
  • Activation and regularisation in neural networks
  • Wrapping up the week

Week 3: Neural networks 2

  • Welcome to week 3
  • Convolutional neural networks
  • Recurrent neural networks
  • Autoencoders
  • Wrapping up the week

Week 4: Transformers and conclusion

  • Welcome to week 4
  • Transformers
  • Microcredential summary
  • Your assignment submission
  • Wrapping up the microcredential

Assessment

You’ll be awarded credits upon passing the final assessment. The assessment will require you to complete a machine learning project on a given data set and will cover the main components of a typical machine learning pipeline. This will include data pre-processing, machine learning method selection and implementation, and performance evaluation.

As part of your assessment, you will also write a concise report (up to 1000 words, excluding tables and figures) to summarise your work and provide an analysis and discussion of the results.

Learn more

Find out more about the Applications of Machine Learning microcredential and apply online at FutureLearn.