Demonstrate the understanding of basic concepts of dealing with different types of data

INSTRUCTIONS TO CANDIDATES

ANSWER ALL QUESTIONS

Learning Outcomes:

• LO1- Demonstrate the understanding of basic concepts of dealing with different types of data – ordinal, categorical, encoding along with collecting, storing and making it ready for processing.

• LO2- Explain the various components of predictive analytics, with the models for regression, classification and clustering to analyse real-life business problems.

• LO3- Implement various models and work on a project life cycle from end to end to solve an analytical problem which translates into a business problem solution using machine learning and artificial intelligence. Assessment Criteria: Weighting 40% 2,000 words Tasks (All tasks are equally weighted): 1. Conceptually and logically describe how, and in which ways as well as in what general forms, humans’ observations and experiments can be transformed into various data in order to be represented? (LO1)

2. Attempt to explain what we do mean by “Machine Learning”? How can, and based on what characteristics, we categorize the concept of “Machine Learning”? (LO2 / LO3) 3. Explain the common as well as the identifiable statistical foundations of supervised and unsupervised algorithms. You are free to utilize Python language for making your explanations more adequate. (LO2 / LO3)

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