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Talk to an Expert| Category | Assignment | Subject | Computer Science |
|---|---|---|---|
| University | New Zealand School of Education (NZSE) | Module Title | BBIM708 / GDDA708 Machine Learning and AI |
| Course | BBIM708 / GDDA708 Machine Learning and AI |
| Programme | Bachelor of Business Information Management and Graduate Diploma in Data Analytics (School of Tech) |
| Institution | New Zealand School of Education (NZSE) |
| Assessment | Assessment 2, Portfolio (individual, open book) |
| Level / Credits | Level 7, 15 credits |
| Weighting | 60% overall weighting, 100 marks (pass mark 50%, all tasks attempted) |
| Due | Week 16 |
| Learning outcomes | LO3 Evaluate machine learning algorithms for their applications to AI; LO4 Apply fine-tuning methods to optimise the performance of machine learning models |
| Referencing | APA 7th edition; submission via Canvas |
This NZSE portfolio is a hands-on machine learning task. In Part A you pick one supervised and one unsupervised algorithm, apply them to an AI problem of your choice with open-source datasets, and evaluate them with a diagnostic visualisation and two metrics. In Part B you optimise a classification model and a regression model using Grid Search, Random Search and 5-fold cross-validation, with screenshots of all code and results.
Select one supervised algorithm (classification or regression) and one unsupervised algorithm. Apply each to a specific AI application (for example house price prediction, customer segmentation or fraud detection) using relevant open-source datasets, then train, evaluate and analyse the models.
Get Answer of BBIM708 Machine Learning and AI Assessment 2
Get Expert Help on WhatsAppChoose supervised classification and regression problems using datasets from sklearn, Kaggle, UCI or your own source, and optimise model performance using Grid Search, Random Search and cross-validation.
Are you working on the BBIM708 Machine Learning and AI Assessment 2 at the New Zealand School of Education (NZSE)? Workingment Academic Writing Platform provides support for students who need help understanding machine learning concepts, evaluating algorithms and improving their assessment structure. Our Assignment Help UK can guide you with supervised and unsupervised learning algorithms, model evaluation, hyperparameter tuning, Grid Search, Random Search and K-Fold Cross-Validation. You can also refer to our Computer Science Assignment Examples for students to understand how to organise ideas, interpret model performance and present technical findings. Our Assignment Writing Services offer guidance with machine learning concepts, data analysis and presenting your work clearly.
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