Learning Approaches
CoreSupervised, unsupervised and common predictive modelling approaches.
A practical module covering supervised and unsupervised learning, model evaluation and predictive modelling.
Included in selected IT Career Switch traineeships as part of a wider career pathway.
AI and data
Data science and analytics modules covering SQL, modelling, reporting and machine learning fundamentals.
Data
Course provider
Course module
Exam/code
2
Related routes
Machine Learning Fundamentals appears across the AI and Data Science routes, helping learners understand how models are trained and evaluated.
01
Supervised and unsupervised learning
02
Model evaluation
03
Predictive models and real datasets
The module covers the practical machine learning ideas learners need before deeper AI, deep learning and deployment work.
Course snapshot
Supervised, unsupervised and common predictive modelling approaches.
Preparing datasets, fitting models and understanding features and labels.
Accuracy, validation, performance metrics and how to compare models.
Applying models to practical datasets and understanding their limits.
Machine learning fundamentals help learners understand how predictive systems are trained, tested and used in practical projects.
Machine Learning Assistant
Junior AI Engineer
Data Scientist Trainee
Analytics Engineer Trainee
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This module links the AI Engineer and Data Science routes by teaching the modelling concepts behind modern AI and analytics work.
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