2.1 Use decision trees for classification and regression problems in comparison with classical methodologies.

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Unit DS07: Machine Learning 

QUALIFI Level 7 Diploma in Data Science
Unit code: K/618/4976 RQF
level: 7

Machine learning algorithms are new generation algorithms used in conjunction with classical predictive modelling methods. In this unit, learners will understand applications of various machine learning algorithms for classification problems.

Learning Outcomes and Assessment Criteria

Learning Outcomes. When awarded credit for this unit, a learner will be able to:

Assessment Criteria. Assessment of this learning outcome will require a learner to demonstrate that they can:

1. Appraise classification methods including Naïve Bayes and the support vector machine algorithm.

1.1 Evaluate different methods of classification and the performance of classifiers.

1.2 Design optimum classification rules to achieve minimum error rates.

2. Apply decision tree and random forest algorithms to classification and regression problems.

2.1 Use decision trees for classification and regression problems in comparison with classical methodologies.

2.2 Analyse concepts of bootstrapping and bagging.

2.3 Apply the random forest method in a range of business and social contexts .

3. Analyse Market Baskets and apply neural networks to classification problems.

3.1 Analyse transactions data for possible associations and derive baskets of associated products.

3.2 Apply neural networks to a classification problem in domains such as speech recognition, image recognition and document categorisation.

Assessment Guidance
To demonstrate all learning outcomes and assessment criteria, each unit should follow the same assessment methodology:

  • Formative: Weekly assignments focussing on knowledge and understanding of technical skills using sample data sets over a period of 3 weeks and participation in weekly live classrooms and discussion groups;
  • Summative: 1. Formal timed exam testing technical knowledge 2. Component of two individual course projects based on real word data analytics

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