7091CEM Machine Learning and Big Data

7091CEM Assignment Help

Machine Learning and Big Data Assignment help

The coursework component will include the development and documenting the development of a machine learning big data system.

Submission
The deadline for submission is 5th April. You should submit your work in the form of a report as a single PDF documenting the decisions made and the process followed during the development of a big data machine learning system. The report should not exceed 2000 words.

Late submissions will be awarded 0 marks. If you have a genuine reason for needing to submit late, you can request an extension from faculty registry (at reception).

Your document should be submitted as a PDF in the form 7091CEMPF.pdf.

Course work Content
The report and hence the development process should be split into the stages outlined below.

a) Big Data for Machine Learning: You will obtain a dataset to work with to create a Machine Learning system. You should clearly evaluate the data set to determine if you feel it is appropriate for your big data machine learning system. If the data needs pre-processing evaluate different approaches and perform pre-processing. (20 Marks)

b) Architecture: You should identify an appropriate architecture for your big data machine learning system. This will include a comparison with other big data machine learning system architectural approaches. You should design the system using an appropriate design representation. At this point you should indication the application of the big data machine learning (this application could be discussed with your tutor). The decision on your application is critical. The application should be practical in terms of the level of functionality as you only have a short-time to develop the system. You should consider factors related to speed of performance, scalability, fault tolerance, technology usage, reliability etc. (20 Marks)

c) Visualisation of data: Determine, implement, and evaluate appropriate approaches for visualise the data you selected. (20 Marks)

d) Machine Learning System Application: You should implement and evaluate the application for your machine learning system. Please note we are looking for an appropriate level of functional given the time constraints. You should evaluate using appropriate techniques whether your application does what it is supposed. (20 Marks)

e) Conclusion and Reflection: Describe what you achieved and how things could be made better. Reflect on the process. (20 Marks)