Machine learning is a key area of artificial intelligence that enables computer systems to learn patterns from data and use those patterns to support predictions, classification and decision-making.

This programme introduces the principles and practical techniques used in machine learning.

Learners can explore how machine learning problems are defined, how datasets are prepared, how models are trained and how their performance is evaluated.

Depending on programme level, learning may include supervised and unsupervised learning, regression, classification, clustering, feature engineering, model evaluation and practical machine learning projects.

This programme can be suitable for:

• Computing and IT students

• Learners progressing from Artificial Intelligence

• Python programming learners

• Aspiring AI and machine learning professionals

• Data-focused learners

• Software developers

• Technology professionals

• Career changers

• Students developing technical AI skills

• Employers developing data and AI capability

• Education and training providers

Machine Learning Fundamentals

Understand the principles, terminology and applications of machine learning.

Data Preparation

Learn how datasets can be explored, cleaned and prepared for machine learning.

Features & Variables

Understand how relevant information can be represented and used by machine learning models.

Supervised Learning

Explore how models can learn from labelled data to support prediction and classification.

Regression

Understand how machine learning can be used to predict numerical outcomes.

Classification

Explore how models can categorise data into different classes.

Unsupervised Learning

Understand approaches for identifying patterns and structures within data without labelled outcomes.

Clustering

Explore how data can be grouped according to similarities and patterns.

Model Training

Understand how machine learning models are trained using available data.

Model Evaluation

Explore how model performance can be measured and interpreted.

Feature Engineering

Depending on programme level, explore techniques for selecting, transforming and creating useful features.

Practical Machine Learning Projects

Apply machine learning techniques to realistic datasets and technology problems.

Prepare. Train. Evaluate. Apply.

Tekelezan focuses on practical machine learning activities alongside the underlying concepts.

Learners can work through:

• Dataset exploration

• Data preparation and cleaning

• Feature selection

• Python-based machine learning exercises

• Model training

• Regression activities

• Classification activities

• Clustering exercises

• Model evaluation

• Prediction tasks

• Visualisation of results

• Model improvement activities

• Practical machine learning projects

The aim is to help learners understand the complete journey from data preparation through to model evaluation and practical application.

Training can be delivered through:

• Classroom-based learning

• Online learning

• Blended delivery

• Python and machine learning workshops

• Technical laboratories

• Guided practical exercises

• Project-based learning

• Intensive programmes

• Employer-focused training

• Bespoke organisational programmes

DURATION

To be confirmed

Programme duration will depend on the programme level, delivery format, technical depth and project requirements.

ENTRY REQUIREMENTS

Programmes can be designed for different levels of experience.

Introductory

Introductory machine learning programmes can focus on concepts and practical demonstrations.

Developing Learners

Basic Python, mathematics and data knowledge can be useful for practical machine learning programmes.

Technical / Advanced

More advanced programmes may recommend previous experience with Python, statistics, mathematics, data analysis or programming.

Specific entry requirements will be confirmed for each programme.

EQUIPMENT & LEARNING RESOURCES

Learners may require:

• A suitable laptop or computer

• Internet access

• Python

• A suitable development environment or notebook platform

• Machine learning libraries and tools where applicable

• Access to datasets

• Learning materials

• Practical project resources

Specific software and hardware requirements will depend on the programme level.

Machine learning skills can support progression into a range of AI, data and technology pathways.

Potential progression areas include:

• Artificial Intelligence

• AI & Automation

• Python Programming

• Data Analysis

• Data Modelling & Data Management

• Software Development

• Cyber Security

• Further AI, data science or computing study

A programme completion certificate may be provided where applicable.

Practical Machine Learning

Develop machine learning skills through practical datasets and exercises.

Python Integration

Use programming skills to explore machine learning concepts and workflows.

Real-World Application

Apply machine learning techniques to realistic problems and datasets.

End-to-End Understanding

Explore the process from data preparation through model evaluation.

AI Progression

Build a foundation for further development in artificial intelligence and data technologies.

Project Experience

Develop practical projects that demonstrate machine learning capability.

Flexible Delivery

Training can be adapted for individuals, employers and partner organisations.

Is this programme suitable for beginners?

Introductory programmes can be designed for beginners, while more technical machine learning programmes may require programming or mathematical knowledge.

Do I need to know Python?

Python knowledge is useful for practical machine learning development. Introductory programmes may introduce the required Python skills.

Do I need mathematics?

Some mathematical and statistical knowledge can be useful, particularly for more technical machine learning programmes.

What types of machine learning will I study?

Depending on programme level, learning can include supervised learning, regression, classification, unsupervised learning and clustering.

Will I work with real datasets?

Where appropriate, learners can work with realistic datasets and practical machine learning scenarios.

Will I build machine learning models?

Yes. Practical programmes can include model development, training and evaluation.

Will I learn how to evaluate models?

Yes. Model evaluation is an important part of understanding whether a machine learning approach is producing useful results.

Will I use Python?

Python can be used for practical machine learning development where appropriate.

Do I need my own laptop?

A suitable laptop or computer is normally recommended for practical learning.

Is there an external examination?

This programme is not currently specified as an external examination programme. Any external certification arrangements will be confirmed where applicable.

Do I receive a certificate?

A programme completion certificate may be provided where applicable.

Can employers arrange machine learning training?

Yes. Tekelezan can discuss employer-focused and bespoke AI and machine learning training.

Can colleges and training providers work with Tekelezan?

Yes. Tekelezan can discuss specialist AI, machine learning and technology delivery partnerships.

How do I enrol?

Use the REGISTER YOUR INTEREST button or contact Tekelezan to discuss the programme and current delivery options.

Artificial Intelligence
Explore the wider principles, applications and technologies of artificial intelligence.

AI & Automation
Explore how AI and automation can be combined to improve processes and workflows.

Python Programming
Develop the programming foundation commonly used for machine learning.

Data Modelling & Data Management
Build knowledge of how data is structured, organised and managed.

Software Development
Develop wider programming and software engineering capability.